Support SAM2 models.

https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/684

.github/workflows/publish.yml

@@ -7,15 +7,19 @@ on:
     paths:
       - "pyproject.toml"
 
+permissions:
+  issues: write
+
 jobs:
   publish-node:
     name: Publish Custom Node to registry
     runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'ltdrdata' }}
     steps:
       - name: Check out code
         uses: actions/checkout@v4
       - name: Publish Custom Node
-        uses: Comfy-Org/publish-node-action@main
+        uses: Comfy-Org/publish-node-action@v1
         with:
           ## Add your own personal access token to your Github Repository secrets and reference it here.
-          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

.gitignore

@@ -7,3 +7,5 @@ subpack
 impact_subpack
 *.txt
 *.yaml
+!requirements.txt
+!LICENSE.txt

README.md

@@ -2,11 +2,15 @@
 
 # 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 
+* V8.18: Support [facebookresearch/sam2](https://github.com/facebookresearch/sam2) models
+* 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. 
@@ -29,12 +33,34 @@ This custom node helps to conveniently enhance images through Detector, Detailer
 * With the addition of wildcard support in FaceDetailer, the structure of DETAILER_PIPE-related nodes and Detailer nodes has changed. There may be malfunctions when using the existing workflow.
 
 
+## How To Install
+
+### **Recommended**
+* Install via [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager).
+
+### **Manual**
+* Navigate to `ComfyUI/custom_nodes` in your terminal (cmd).
+* Clone the repository under the `custom_nodes` directory using the following command:
+  ```
+  git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack comfyui-impact-pack
+  cd comfyui-impact-pack
+  ```
+* Install dependencies in your Python environment.
+    * For Windows Portable, run the following command inside `ComfyUI\custom_nodes\comfyui-impact-pack`:
+        ```
+        ..\..\..\python_embeded\python.exe -m pip install -r requirements.txt
+        ```
+    * If using venv or conda, activate your Python environment first, then run:
+        ```
+        pip install -r requirements.txt
+        ```
+
+### Companion Pack
+* If you need the `Ultralytics Detector Provider` to use various YOLO detection models, you should also install [ComfyUI-Impact-Subpack](https://github.com/ltdrdata/ComfyUI-Impact-Subpack).
+
 ## Custom Nodes
 ### [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**.
+  * `SAMLoader (Impact)` - Loads the SAM model.
   * `ONNXDetectorProvider` - Loads the ONNX model to provide BBOX_DETECTOR.
   * `CLIPSegDetectorProvider` - Wrapper for CLIPSeg to provide BBOX_DETECTOR.
     * You need to install the ComfyUI-CLIPSeg node extension.
@@ -45,10 +71,13 @@ This custom node helps to conveniently enhance images through Detector, Detailer
     * As a result, it outputs the `combined_mask`, which is a unified mask, and `batch_masks`, which are multiple masks grouped together in batch form.
     * While `batch_masks` may not be completely separated, it provides functionality to perform some level of segmentation.
   * `Simple Detector (SEGS)` - Operating primarily with `BBOX_DETECTOR`, and with the additional provision of `SAM_MODEL` or `SEGM_DETECTOR`, this node internally generates improved SEGS through mask operations on both *bbox* and *silhouette*. It serves as a convenient tool to simplify a somewhat intricate workflow.
+  * `Simple Detector for Video (SEGS)` – Performs detection on videos composed of image frames. Instead of using a single mask, it performs detection individually on each image frame and generates a SEGS object with a batch of masks. 
+  * `SAM2 Video Detector (SEGS)` – Similar to `Simple Detector for Video (SEGS)`, but utilizes SAM2’s video tracking technology to generate a SEGS object with a batch of masks. 
+      * To use this node, you must select a SAM2 model in the SAMLoader.
 
 ### 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
@@ -68,13 +97,15 @@ 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.
   * `DetailerDebug (SEGS)` - Refines the image based on SEGS. Additionally, it provides the ability to monitor the cropped image and the refined image of the cropped image.
     * To prevent regeneration caused by the seed that does not change every time when using 'external_seed', please disable the 'seed random generate' option in the 'Detailer...' node.
   * `MASK to SEGS` - Generates SEGS based on the mask.
-  * `MASK to SEGS For AnimateDiff` - Generates SEGS based on the mask for AnimateDiff.
+  * `MASK to SEGS For Video` - Generates SEGS based on the mask for Video. (Renamed from `MASK to SEGS For AnimateDiff`)
     * When using a single mask, convert it to SEGS to apply it to the entire frame.
     * When using a batch mask, the contour fill feature is disabled.
   * `MediaPipe FaceMesh to SEGS` - Separate each landmark from the mediapipe facemesh image to create labeled SEGS.
@@ -89,6 +120,7 @@ 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.
@@ -104,8 +136,11 @@ This custom node helps to conveniently enhance images through Detector, Detailer
   * `SEGS Filter (label)` - This node filters SEGS based on the label of the detected areas. 
   * `SEGS Filter (ordered)` - This node sorts SEGS based on size and position and retrieves SEGs within a certain range. 
   * `SEGS Filter (range)` - This node retrieves only SEGs from SEGS that have a size and position within a certain range.
+  * `SEGS Filter (non max suppression)` - This node filters SEGS by removing those with high overlap based on the Intersection over Union (IoU) threshold, keeping only the most confident detections.
+  * `SEGS Filter (intersection)` - This node filters segs1, keeping only the SEGS that do not significantly overlap with any SEGS in segs2, based on the Intersection over Area (IoA) threshold.
   * `SEGS Assign (label)` - Assign labels sequentially to SEGS. This node is useful when used with `[LAB]` of FaceDetailer.
   * `SEGSConcat` - Concatenate segs1 and segs2. If source shape of segs1 and segs2 are different from segs2 will be ignored.
+  * `SEGS Merge` - SEGS contains multiple SEGs. SEGS Merge integrates several SEGs into a single merged SEG. The label is changed to `merged` and the confidence becomes the minimum confidence. The applied controlnet and cropped_image are removed.
   * `Picker (SEGS)` - Among the input SEGS, you can select a specific SEG through a dialog. If no SEG is selected, it outputs an empty SEGS. Increasing the batch_size of SEGSDetailer can be used for the purpose of selecting from the candidates.
   * `Set Default Image For SEGS` - Set a default image for SEGS. SEGS with images set this way do not need to have a fallback image set. When override is set to false, the original image is preserved.
   * `Remove Image from SEGS` - Remove the image set for the SEGS that has been configured by "Set Default Image for SEGS" or SEGSDetailer. When the image for the SEGS is removed, the Detailer node will operate based on the currently processed image instead of the SEGS. 
@@ -158,6 +193,10 @@ This custom node helps to conveniently enhance images through Detector, Detailer
   * `PreviewDetailerHook` - Connecting this hook node helps provide assistance for viewing previews whenever SEGS Detailing tasks are completed. When working with a large number of SEGS, such as Make Tile SEGS, it allows for monitoring the situation as improvements progress incrementally.
     * Since this is the hook applied when pasting onto the original image, it has no effect on nodes like `SEGSDetailer`.
   * `VariationNoiseDetailerHookProvider` - Apply variation seed to the detailer. It can be applied in multiple stages through combine.
+  * `CustomSamplerDetailerHookProvider` - Apply a hook that allows you to use a custom sampler in the Detailer nodes. When using `DetailerHookCombine`, the sampler from the first hook is applied.
+  * `LamaRemoverDetailerHookProvider` – Applies Lama Remover to the upscaled image during the detailing stage. If `skip_sampling` is set to True, Lama Remover can be used alone without the detailing stage, allowing it to simply remove detected regions.
+      * Not applicable for **AnimateDiff** detailers. When using `DetailerHookCombine`, `skip_sampling` is only applied if it is set to `True` for all hooks.
+      * To use this node, the node pack at [Layer-norm/comfyui-lama-remover](https://github.com/Layer-norm/comfyui-lama-remover) must be installed.
 
 ### Iterative Upscale nodes
   * `Iterative Upscale (Latent/on Pixel Space)` - The upscaler takes the input upscaler and splits the scale_factor into steps, then iteratively performs upscaling. 
@@ -220,7 +259,7 @@ This custom node helps to conveniently enhance images through Detector, Detailer
 
 
 ### Impact KSampler
-  * These samplers support basic_pipe and AYS scheduler
+  * These samplers support basic_pipe and AYS/OSS/GITS scheduler
   * `KSampler (pipe)` - pipe version of KSampler
   * `KSampler (advanced/pipe)` - pipe version of KSamplerAdvacned
   * When converting the scheduler widget to input, refer to the `Impact Scheduler Adapter` node to resolve compatibility issues.
@@ -236,11 +275,12 @@ 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.
+  * `Select Nth Item (Any list)` - Selects the Nth item from a list. If the index is out of range, it returns the last item in the list. 
 
 ### Logics (experimental) 
   * These nodes are experimental nodes designed to implement the logic for loops and dynamic switching.
-  * `ImpactCompare`, `ImpactConditionalBranch`, `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 +311,7 @@ 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 +324,7 @@ 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 +332,41 @@ 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 +389,16 @@ 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)
@@ -491,3 +496,5 @@ BlenderNeok/[ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) - The
 WASasquatch/[was-node-suite-comfyui](https://github.com/WASasquatch/was-node-suite-comfyui) - A powerful custom node extensions of ComfyUI.
 
 Trung0246/[ComfyUI-0246](https://github.com/Trung0246/ComfyUI-0246) - Nice bypass hack!
+
+Layer-norm/[comfyui-lama-remover](https://github.com/Layer-norm/comfyui-lama-remover) - Required for using `LamaRemoverDetailerHook`.

__init__.py

@@ -13,39 +13,16 @@ 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)
 
 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 +40,10 @@ 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 +93,7 @@ NODE_CLASS_MAPPINGS = {
     "FromDetailerPipe": FromDetailerPipe,
     "FromDetailerPipe_v2": FromDetailerPipe_v2,
     "FromDetailerPipeSDXL": FromDetailerPipe_SDXL,
+    "AnyPipeToBasic": AnyPipeToBasic,
     "ToBasicPipe": ToBasicPipe,
     "FromBasicPipe": FromBasicPipe,
     "FromBasicPipe_v2": FromBasicPipe_v2,
@@ -144,6 +122,8 @@ NODE_CLASS_MAPPINGS = {
     "UnsamplerHookProvider": UnsamplerHookProvider,
     "CoreMLDetailerHookProvider": CoreMLDetailerHookProvider,
     "PreviewDetailerHookProvider": PreviewDetailerHookProvider,
+    "CustomSamplerDetailerHookProvider": CustomSamplerDetailerHookProvider,
+    "LamaRemoverDetailerHookProvider": LamaRemoverDetailerHookProvider,
 
     "DetailerHookCombine": DetailerHookCombine,
     "NoiseInjectionDetailerHookProvider": NoiseInjectionDetailerHookProvider,
@@ -158,6 +138,8 @@ NODE_CLASS_MAPPINGS = {
     "BitwiseAndMask": BitwiseAndMask,
     "SubtractMask": SubtractMask,
     "AddMask": AddMask,
+    "MaskRectArea": MaskRectArea,
+    "MaskRectAreaAdvanced": MaskRectAreaAdvanced,
     "ImpactSegsAndMask": SegsBitwiseAndMask,
     "ImpactSegsAndMaskForEach": SegsBitwiseAndMaskForEach,
     "EmptySegs": EmptySEGS,
@@ -177,6 +159,7 @@ NODE_CLASS_MAPPINGS = {
     "SegmDetectorSEGS": SegmDetectorForEach,
     "ONNXDetectorSEGS": BboxDetectorForEach,
     "ImpactSimpleDetectorSEGS_for_AD": SimpleDetectorForAnimateDiff,
+    "ImpactSAM2VideoDetectorSEGS": SAM2VideoDetectorSEGS,
     "ImpactSimpleDetectorSEGS": SimpleDetectorForEach,
     "ImpactSimpleDetectorSEGSPipe": SimpleDetectorForEachPipe,
     "ImpactControlNetApplySEGS": ControlNetApplySEGS,
@@ -238,6 +221,7 @@ NODE_CLASS_MAPPINGS = {
     "ImpactSEGSConcat": SEGSConcat,
     "ImpactSEGSPicker": SEGSPicker,
     "ImpactMakeTileSEGS": MakeTileSEGS,
+    "ImpactSEGSMerge": SEGSMerge,
 
     "SEGSDetailerForAnimateDiff": SEGSDetailerForAnimateDiff,
 
@@ -250,8 +234,10 @@ NODE_CLASS_MAPPINGS = {
     "ImpactImageBatchToImageList": ImageBatchToImageList,
     "ImpactMakeImageList": MakeImageList,
     "ImpactMakeImageBatch": MakeImageBatch,
+    "ImpactMakeAnyList": MakeAnyList,
     "ImpactMakeMaskList": MakeMaskList,
     "ImpactMakeMaskBatch": MakeMaskBatch,
+    "ImpactSelectNthItemOfAnyList": NthItemOfAnyList,
 
     "RegionalSampler": RegionalSampler,
     "RegionalSamplerAdvanced": RegionalSamplerAdvanced,
@@ -265,6 +251,8 @@ NODE_CLASS_MAPPINGS = {
     "ImpactSEGSLabelFilter": SEGSLabelFilter,
     "ImpactSEGSRangeFilter": SEGSRangeFilter,
     "ImpactSEGSOrderedFilter": SEGSOrderedFilter,
+    "ImpactSEGSIntersectionFilter": SEGSIntersectionFilter,
+    "ImpactSEGSNMSFilter": SEGSNMSFilter,
 
     "ImpactCompare": ImpactCompare,
     "ImpactConditionalBranch": ImpactConditionalBranch,
@@ -274,6 +262,7 @@ NODE_CLASS_MAPPINGS = {
     "ImpactLogicalOperators": ImpactLogicalOperators,
     "ImpactInt": ImpactInt,
     "ImpactFloat": ImpactFloat,
+    "ImpactBoolean": ImpactBoolean,
     "ImpactValueSender": ImpactValueSender,
     "ImpactValueReceiver": ImpactValueReceiver,
     "ImpactImageInfo": ImpactImageInfo,
@@ -285,6 +274,7 @@ NODE_CLASS_MAPPINGS = {
     "StringListToString": StringListToString,
     "WildcardPromptFromString": WildcardPromptFromString,
     "ImpactExecutionOrderController": ImpactExecutionOrderController,
+    "ImpactListBridge": ImpactListBridge,
 
     "RemoveNoiseMask": RemoveNoiseMask,
 
@@ -315,11 +305,12 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "BboxDetectorSEGS": "BBOX Detector (SEGS)",
     "SegmDetectorSEGS": "SEGM Detector (SEGS)",
     "ONNXDetectorSEGS": "ONNX Detector (SEGS/legacy) - use BBOXDetector",
-    "ImpactSimpleDetectorSEGS_for_AD": "Simple Detector for AnimateDiff (SEGS)",
+    "ImpactSimpleDetectorSEGS_for_AD": "Simple Detector for Video (SEGS)",
+    "ImpactSAM2VideoDetectorSEGS": "SAM2 Video Detector (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)",
@@ -327,7 +318,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "SegsToCombinedMask": "SEGS to MASK (combined)",
     "MediaPipeFaceMeshToSEGS": "MediaPipe FaceMesh to SEGS",
     "MaskToSEGS": "MASK to SEGS",
-    "MaskToSEGS_for_AnimateDiff": "MASK to SEGS for AnimateDiff",
+    "MaskToSEGS_for_AnimateDiff": "MASK to SEGS for Video",
     "BitwiseAndMaskForEach": "Pixelwise(SEGS & SEGS)",
     "SubtractMaskForEach": "Pixelwise(SEGS - SEGS)",
     "ImpactSegsAndMask": "Pixelwise(SEGS & MASK)",
@@ -335,13 +326,15 @@ 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)",
     "DetailerForEachDebug": "DetailerDebug (SEGS)",
     "DetailerForEachDebugPipe": "DetailerDebug (SEGS/pipe)",
-    "SEGSDetailerForAnimateDiff": "SEGSDetailer For AnimateDiff (SEGS/pipe)",
-    "DetailerForEachPipeForAnimateDiff": "Detailer For AnimateDiff (SEGS/pipe)",
+    "SEGSDetailerForAnimateDiff": "SEGSDetailer For Video (SEGS/pipe)",
+    "DetailerForEachPipeForAnimateDiff": "Detailer For Video (SEGS/pipe)",
     "SEGSUpscaler": "Upscaler (SEGS)",
     "SEGSUpscalerPipe": "Upscaler (SEGS/pipe)",
 
@@ -358,6 +351,7 @@ 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)",
@@ -375,11 +369,14 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImpactSEGSLabelFilter": "SEGS Filter (label)",
     "ImpactSEGSRangeFilter": "SEGS Filter (range)",
     "ImpactSEGSOrderedFilter": "SEGS Filter (ordered)",
+    "ImpactSEGSIntersectionFilter": "SEGS Filter (intersection)",
+    "ImpactSEGSNMSFilter": "SEGS Filter (non max suppression)",
     "ImpactSEGSConcat": "SEGS Concat",
     "ImpactSEGSToMaskList": "SEGS to Mask List",
     "ImpactSEGSToMaskBatch": "SEGS to Mask Batch",
     "ImpactSEGSPicker": "Picker (SEGS)",
     "ImpactMakeTileSEGS": "Make Tile SEGS",
+    "ImpactSEGSMerge": "SEGS Merge",
 
     "ImpactDecomposeSEGS": "Decompose (SEGS)",
     "ImpactAssembleSEGS": "Assemble (SEGS)",
@@ -403,6 +400,7 @@ 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 +411,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImpactMakeImageBatch": "Make Image Batch",
     "ImpactMakeMaskList": "Make Mask List",
     "ImpactMakeMaskBatch": "Make Mask Batch",
+    "ImpactMakeAnyList": "Make List (Any)",
+    "ImpactSelectNthItemOfAnyList": "Select Nth Item (Any list)",
 
     "ImpactStringSelector": "String Selector",
     "StringListToString": "String List to String",
@@ -472,19 +472,6 @@ 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

example_workflows/2-MaskDetailer.json

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

install.py

@@ -5,7 +5,6 @@ import subprocess
 import threading
 import locale
 import traceback
-import re
 
 
 if sys.argv[0] == 'install.py':
@@ -13,14 +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 +67,38 @@ 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("[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})")
@@ -291,9 +106,22 @@ try:
 
         impact.config.write_config()
 
+        # Remove legacy subpack
+        try:
+            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.")
+        except:
+            print(f"ERROT: Failed to delete legacy subpack '{subpack_path}'\nPlease delete the folder after terminate ComfyUI.")
 
     install()
 
-except Exception as e:
+except Exception:
     print("[ERROR] ComfyUI-Impact-Pack: Dependency installation has failed. Please install manually.")
     traceback.print_exc()

js/comboBoolMigration.js

@@ -1,35 +0,0 @@
-import { ComfyApp, app } from "../../scripts/app.js";
-
-let conflict_check = undefined;
-
-app.registerExtension({
-	name: "Comfy.impact.comboBoolMigration",
-
-	nodeCreated(node, app) {
-		for(let i in node.widgets) {
-			let widget = node.widgets[i];
-
-			if(conflict_check == undefined) {
-				conflict_check = !!app.extensions.find((ext) => ext.name === "Comfy.comboBoolMigration");
-			}
-
-			if(conflict_check)
-				return;
-
-			if(widget.type == "toggle") {
-				let value = widget.value;
-
-				var v = Object.getOwnPropertyDescriptor(widget, 'value');
-				if(!v) {
-					Object.defineProperty(widget, "value", {
-						set: (value) => {
-								delete widget.value;
-								widget.value = value == true || value == widget.options.on;
-							},
-						get: () => { return value; }
-					});
-				}
-			}
-		}
-	}
-});

js/common.js

@@ -3,6 +3,48 @@ import { app } from "../../scripts/app.js";
 
 let original_show = app.ui.dialog.show;
 
+export function customAlert(message) {
+	try {
+		app.extensionManager.toast.addAlert(message);
+	}
+	catch {
+		alert(message);
+	}
+}
+
+export function isBeforeFrontendVersion(compareVersion) {
+    try {
+        const frontendVersion = window['__COMFYUI_FRONTEND_VERSION__'];
+        if (typeof frontendVersion !== 'string') {
+            return false;
+        }
+
+        function parseVersion(versionString) {
+            const parts = versionString.split('.').map(Number);
+            return parts.length === 3 && parts.every(part => !isNaN(part)) ? parts : null;
+        }
+
+        const currentVersion = parseVersion(frontendVersion);
+        const comparisonVersion = parseVersion(compareVersion);
+
+        if (!currentVersion || !comparisonVersion) {
+            return false;
+        }
+
+        for (let i = 0; i < 3; i++) {
+            if (currentVersion[i] > comparisonVersion[i]) {
+                return false;
+            } else if (currentVersion[i] < comparisonVersion[i]) {
+                return true;
+            }
+        }
+
+        return false;
+    } catch {
+        return true;
+    }
+}
+
 function dialog_show_wrapper(html) {
 	if (typeof html === "string") {
 		if(html.includes("IMPACT-PACK-SIGNAL: STOP CONTROL BRIDGE")) {

js/impact-pack.js

@@ -1,6 +1,13 @@
 import { ComfyApp, app } from "../../scripts/app.js";
 import { ComfyDialog, $el } from "../../scripts/ui.js";
 import { api } from "../../scripts/api.js";
+import { customAlert, isBeforeFrontendVersion } from "./common.js";
+
+const is_legacy_front = () => isBeforeFrontendVersion('1.16.9');
+
+if(is_legacy_front()) {
+	customAlert("An outdated version(<1.16.9) of the `comfyui-frontend-package` is installed. It is not compatible with the current version of the Impact Pack.");
+}
 
 let wildcards_list = [];
 async function load_wildcards() {
@@ -93,7 +100,7 @@ const input_dirty = {};
 const output_tracking = {};
 
 function progressExecuteHandler(event) {
-	if(event.detail.output.aux){
+	if(event.detail?.output?.aux){
 		const id = event.detail.node;
 		if(input_tracking.hasOwnProperty(id)) {
 			if(input_tracking.hasOwnProperty(id) && input_tracking[id][0] != event.detail.output.aux[0]) {
@@ -222,6 +229,31 @@ 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;
@@ -248,7 +280,7 @@ app.registerExtension({
 				}
 				else {
 					const node = app.graph.getNodeById(link_info.origin_id);
-					slot_type = node.outputs[link_info.origin_slot].type;
+					slot_type = node.outputs[link_info.origin_slot]?.type;
 				}
 
 				this.inputs[0].type = slot_type;
@@ -299,6 +331,32 @@ app.registerExtension({
 			}
 		}
 
+		if(nodeData.name == "ImpactSelectNthItemOfAnyList") {
+			const onConnectionsChange = nodeType.prototype.onConnectionsChange;
+			nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
+				if(!link_info || this.inputs[0].type != '*')
+					return;
+
+				if(index >= 2)
+					return;
+
+				// assign type
+				let slot_type = '*';
+
+				if(type == 2) {
+					slot_type = link_info.type;
+				}
+				else {
+					const node = app.graph.getNodeById(link_info.origin_id);
+					slot_type = node.outputs[link_info.origin_slot].type;
+				}
+
+				this.inputs[0].type = slot_type;
+				this.outputs[0].type = slot_type;
+				this.outputs[0].label = slot_type;
+			}
+		}
+
 		if(nodeData.name === 'ImpactInversedSwitch') {
 			nodeData.output = ['*'];
 			nodeData.output_is_list = [false];
@@ -312,12 +370,12 @@ app.registerExtension({
 				if(type == 2) {
 					// connect output
 					if(connected){
-						if(app.graph._nodes_by_id[link_info.target_id].type == 'Reroute') {
+						if(app.graph._nodes_by_id[link_info.target_id]?.type == 'Reroute') {
 							app.graph._nodes_by_id[link_info.target_id].disconnectInput(link_info.target_slot);
 						}
 
 						if(this.outputs[0].type == '*'){
-							if(link_info.type == '*') {
+							if(link_info.type == '*' && app.graph.getNodeById(link_info.target_id).slots[link_info.target_slot].type != '*') {
 								app.graph._nodes_by_id[link_info.target_id].disconnectInput(link_info.target_slot);
 							}
 							else {
@@ -334,15 +392,19 @@ app.registerExtension({
 					}
 				}
 				else {
-					if(app.graph._nodes_by_id[link_info.origin_id].type == 'Reroute')
+					if(app.graph._nodes_by_id[link_info.origin_id]?.type == 'Reroute')
 						this.disconnectInput(link_info.target_slot);
 
 					// 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 == '*') {
+						if(origin_type==undefined) {
+							return; // fallback
+						}
+
+						if(origin_type == '*' && app.graph.getNodeById(link_info.origin_id).slots[link_info.origin_slot].type != '*') {
 							this.disconnectInput(link_info.target_slot);
 							return;
 						}
@@ -353,7 +415,7 @@ app.registerExtension({
 						}
 
 						this.outputs[0].type = origin_type;
-						this.outputs[0].name = origin_type;
+						this.outputs[0].name = 'output1';
 					}
 
 					return;
@@ -366,24 +428,31 @@ app.registerExtension({
 						!stackTrace.includes('LGraphNode.prototype.connect') && // for touch device
 						!stackTrace.includes('LGraphNode.connect') && // for mouse device
 						!stackTrace.includes('loadGraphData')) {
-							if(this.outputs[link_info.origin_slot].links.length == 0)
+							if(this.outputs[link_info.origin_slot].links.length == 0) {
 								this.removeOutput(link_info.origin_slot);
+							}
 					}
 				}
 
 				let slot_i = 1;
 				for (let i = 0; i < this.outputs.length; i++) {
 					this.outputs[i].name = `output${slot_i}`
+					if (this.outputs[i].slot_index === undefined) {
+						this.outputs[i].slot_index = i;
+					}
 					slot_i++;
 				}
 
-				let last_slot = this.outputs[this.outputs.length - 1];
-				if (last_slot.slot_index == link_info.origin_slot) {
-					this.addOutput(`output${slot_i}`, this.outputs[0].type);
+				if(connected) {
+					// NOTE: node.slot_index is different with link_info.origin_slot
+					let last_slot_index = this.outputs.length - 1;
+					if (last_slot_index == link_info.origin_slot) {
+						this.addOutput(`output${slot_i}`, this.outputs[0].type);
+					}
 				}
 
 				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 +463,7 @@ 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 +480,10 @@ app.registerExtension({
 				input_name = "mask";
 				break;
 
+			case 'ImpactMakeAnyList':
+				input_name = "value";
+				break;
+
 			case 'ImpactSEGSConcat':
 				input_name = "segs";
 				break;
@@ -438,6 +511,23 @@ app.registerExtension({
 
 			const onConnectionsChange = nodeType.prototype.onConnectionsChange;
 			nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
+				const stackTrace = new Error().stack;
+				if(stackTrace.includes('loadGraphData')) {
+					if(this.widgets?.[0]) {
+						this.widgets[0].options.max = this.inputs.length-3;
+						this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max);
+					}
+					return;
+				}
+
+				if(stackTrace.includes('pasteFromClipboard')) {
+					if(this.widgets?.[0]) {
+						this.widgets[0].options.max = this.inputs.length-3;
+						this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max);
+					}
+					return;
+				}
+
 				if(!link_info)
 					return;
 
@@ -449,7 +539,7 @@ app.registerExtension({
 						}
 
 						if(this.outputs[0].type == '*'){
-							if(link_info.type == '*') {
+							if(link_info.type == '*' && app.graph.getNodeById(link_info.target_id).slots[link_info.target_slot].type != '*') {
 								app.graph._nodes_by_id[link_info.target_id].disconnectInput(link_info.target_slot);
 							}
 							else {
@@ -479,9 +569,13 @@ 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;
-
-						if(origin_type == '*') {
+						let origin_type = node.outputs[link_info.origin_slot]?.type;
+						if(link_info.target_slot == 0 && this.inputs.length > 3) {  // NOTE: widgets are regarded as input since new front
+								origin_type = this.inputs[1].type;
+								node.connect(link_info.origin_slot, node.id, 'input1');
+						}
+						
+						if(origin_type == '*' && app.graph.getNodeById(link_info.origin_id).slots[link_info.origin_slot].type != '*') {
 							this.disconnectInput(link_info.target_slot);
 							return;
 						}
@@ -499,15 +593,8 @@ app.registerExtension({
 				}
 
 				let select_slot = this.inputs.find(x => x.name == "select");
-				let mode_slot = this.inputs.find(x => x.name == "sel_mode");
-
-				let converted_count = 0;
-				converted_count += select_slot?1:0;
-				converted_count += mode_slot?1:0;
-
-				if (!connected && (this.inputs.length > 1+converted_count)) {
-					const stackTrace = new Error().stack;
 
+				if (!connected && (this.inputs.length > 3)) {
 					if(
 						!stackTrace.includes('LGraphNode.prototype.connect') && // for touch device
 						!stackTrace.includes('LGraphNode.connect') && // for mouse device
@@ -517,6 +604,7 @@ app.registerExtension({
 					}
 				}
 
+
 				let slot_i = 1;
 				for (let i = 0; i < this.inputs.length; i++) {
 					let input_i = this.inputs[i];
@@ -526,18 +614,13 @@ app.registerExtension({
 					}
 				}
 
-				let last_slot = this.inputs[this.inputs.length - 1];
-				if (
-					(last_slot.name == 'select' && last_slot.name != 'sel_mode' && this.inputs[this.inputs.length - 2].link != undefined)
-					|| (last_slot.name != 'select' && last_slot.name != 'sel_mode' && last_slot.link != undefined)) {
-						this.addInput(`${input_name}${slot_i}`, this.outputs[0].type);
+				if(connected) {
+					this.addInput(`${input_name}${slot_i}`, this.outputs[0].type);
 				}
 
-				if(this.widgets) {
-					this.widgets[0].options.max = select_slot?this.inputs.length-1:this.inputs.length;
+				if(this.widgets?.[0]) {
+					this.widgets[0].options.max = this.inputs.length-3;
 					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)
-						this.widgets[0].value = 1;
 				}
 			}
 		}
@@ -579,17 +662,19 @@ app.registerExtension({
 		}
 
 		if(node.comfyClass == "ImpactSEGSLabelFilter" || node.comfyClass == "SEGSLabelFilterDetailerHookProvider") {
+			node.widgets[0].callback = (value, canvas, node, pos, e) => {
+				if(node) {
+					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 +744,20 @@ app.registerExtension({
 					break;
 			}
 
+			node.widgets[combo_id+1].callback = (value, canvas, node, pos, e) => {
+					if(node) {
+						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 +769,24 @@ app.registerExtension({
 			});
 
 			if(has_lora) {
+				node.widgets[combo_id].callback = (value, canvas, node, pos, e) => {
+					if(node) {
+						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"; }
@@ -724,14 +811,20 @@ app.registerExtension({
 			// mode combo
 			Object.defineProperty(mode_widget, "value", {
 				set: (value) => {
-						node._mode_value = value == true || value == "Populate";
-						populated_text_widget.inputEl.disabled = value == true || value == "Populate";
+						if(value == true)
+							node._mode_value = "populate";
+						else if(value == false)
+							node._mode_value = "fixed";
+						else
+							node._mode_value = value; // combo value
+
+						populated_text_widget.inputEl.disabled = node._mode_value == 'populate';
 					},
 				get: () => {
 						if(node._mode_value != undefined)
 							return node._mode_value;
 						else
-							return true;
+							return 'populate';
 					 }
 			});
 		}

js/impact-sam-editor.js

@@ -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) {

js/impact-wildcard.js

@@ -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');
-    });
-}

js/mask-rect-area-advanced.js

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

js/mask-rect-area.js

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

modules/impact/animatediff_nodes.py

@@ -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": {
@@ -45,6 +45,8 @@ class SEGSDetailerForAnimateDiff:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is applied specifically to SEGS rather than the entire image. To apply it to the entire image, use the 'SEGS Paste' node.\nAs a specialized detailer node for improving video details, such as in AnimateDiff, this node can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames."
+
     @staticmethod
     def do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
                   denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, noise_mask_feather=0, scheduler_func_opt=None):
@@ -60,7 +62,7 @@ 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 +96,18 @@ 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 +150,7 @@ 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": {
@@ -161,6 +168,8 @@ class DetailerForEachPipeForAnimateDiff:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is a specialized detailer node for enhancing video details, such as in AnimateDiff. It can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames."
+
     @staticmethod
     def doit(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
              denoise, feather, basic_pipe, refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None,

modules/impact/bridge_nodes.py

@@ -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,

modules/impact/config.py

@@ -1,10 +1,10 @@
 import configparser
 import os
 
-version_code = [7, 5, 1]
+version_code = [8, 18]
 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")

modules/impact/core.py

@@ -1,16 +1,15 @@
-import copy
 import os
 import warnings
 
-import numpy
 import torch
+from sam2.sam2_image_predictor import SAM2ImagePredictor
 from segment_anything import SamPredictor
 
 from comfy_extras.nodes_custom_sampler import Noise_RandomNoise
 from impact.utils import *
 from collections import namedtuple
 import numpy as np
-from skimage.measure import label
+from PIL import ImageOps, Image
 
 import nodes
 import comfy_extras.nodes_upscale_model as model_upscale
@@ -24,6 +23,11 @@ from comfy import model_management
 from impact import utils
 from impact import impact_sampling
 from concurrent.futures import ThreadPoolExecutor
+import inspect
+from collections import OrderedDict
+from sam2.build_sam import build_sam2, build_sam2_video_predictor
+import torch.nn.functional as F
+
 
 try:
     from comfy_extras import nodes_differential_diffusion
@@ -39,10 +43,13 @@ 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]', 'OSS FLUX', 'OSS Wan']
 
 
 def is_execution_model_version_supported():
@@ -64,13 +71,20 @@ 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
 
 
 def erosion_mask(mask, grow_mask_by):
-    mask = make_2d_mask(mask)
+    mask = utils.make_2d_mask(mask)
 
     w = mask.shape[1]
     h = mask.shape[0]
@@ -126,7 +140,7 @@ def mix_noise(from_noise, to_noise, strength, variation_method):
 
 class REGIONAL_PROMPT:
     def __init__(self, mask, sampler, variation_seed=0, variation_strength=0.0, variation_method='linear'):
-        mask = make_2d_mask(mask)
+        mask = utils.make_2d_mask(mask)
 
         self.mask = mask
         self.sampler = sampler
@@ -186,7 +200,7 @@ def create_segmasks(results):
 
 
 def gen_detection_hints_from_mask_area(x, y, mask, threshold, use_negative):
-    mask = make_2d_mask(mask)
+    mask = utils.make_2d_mask(mask)
 
     points = []
     plabs = []
@@ -231,7 +245,8 @@ 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)
@@ -304,7 +319,10 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
     print(f"Detailer: segment upscale for ({bbox_w, bbox_h}) | crop region {w, h} x {upscale} -> {new_w, new_h}")
 
     # upscale
-    upscaled_image = tensor_resize(image, new_w, new_h)
+    upscaled_image = utils.tensor_resize(image, new_w, new_h)
+
+    if detailer_hook is not None:
+        upscaled_image = detailer_hook.post_upscale(upscaled_image, noise_mask)
 
     cnet_pils = None
     if control_net_wrapper is not None:
@@ -313,56 +331,75 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
         cnet_pils.extend(cnet_pils2)
 
     # 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)
-    else:
-        latent_image = to_latent_image(upscaled_image, vae)
-        if noise_mask is not None:
-            latent_image['noise_mask'] = noise_mask
-
-    if detailer_hook is not None:
-        latent_image = detailer_hook.post_encode(latent_image)
-
-    refined_latent = latent_image
-
-    # ksampler
-    for i in range(0, cycle):
-        if detailer_hook is not None:
-            if detailer_hook is not None:
-                detailer_hook.set_steps((i, cycle))
-
-            refined_latent = detailer_hook.cycle_latent(refined_latent)
-
-            model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \
-                detailer_hook.pre_ksample(model, seed+i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)
-            noise, is_touched = detailer_hook.get_custom_noise(seed+i, torch.zeros(latent_image['samples'].size()), is_touched=False)
-            if not is_touched:
-                noise = None
+    if detailer_hook is None or not detailer_hook.get_skip_sampling():
+        if noise_mask is not None and inpaint_model:
+            imc_encode = nodes.InpaintModelConditioning().encode
+            if 'noise_mask' in inspect.signature(imc_encode).parameters:
+                positive, negative, latent_image = imc_encode(positive, negative, 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:
-            model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \
-                model, seed + i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise
-            noise = None
+            latent_image = utils.to_latent_image(upscaled_image, vae, vae_tiled_encode=vae_tiled_encode)
+            if noise_mask is not None:
+                latent_image['noise_mask'] = noise_mask
 
-        refined_latent = impact_sampling.ksampler_wrapper(model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2,
-                                                          refined_latent, denoise2, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative,
-                                                          noise=noise, scheduler_func=scheduler_func)
+        if detailer_hook is not None:
+            latent_image = detailer_hook.post_encode(latent_image)
 
-    if detailer_hook is not None:
-        refined_latent = detailer_hook.pre_decode(refined_latent)
+        refined_latent = latent_image
 
-    # 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, )
+        sampler_opt=None
+        if detailer_hook is not None:
+            sampler_opt = detailer_hook.get_custom_sampler()
+
+        # ksampler
+        for i in range(0, cycle):
+            if detailer_hook is not None:
+                if detailer_hook is not None:
+                    detailer_hook.set_steps((i, cycle))
+
+                refined_latent = detailer_hook.cycle_latent(refined_latent)
+
+                model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \
+                    detailer_hook.pre_ksample(model, seed+i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)
+                noise, is_touched = detailer_hook.get_custom_noise(seed+i, torch.zeros(latent_image['samples'].size()), is_touched=False)
+                if not is_touched:
+                    noise = None
+            else:
+                model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \
+                    model, seed + i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise
+                noise = None
+
+            refined_latent = impact_sampling.ksampler_wrapper(model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2,
+                                                              refined_latent, denoise2, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative,
+                                                              noise=noise, scheduler_func=scheduler_func, sampler_opt=sampler_opt)
+
+        if detailer_hook is not None:
+            refined_latent = detailer_hook.pre_decode(refined_latent)
+
+        # non-latent downscale - latent downscale cause bad quality
+        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")
+    else:
+        # skipped
+        refined_image = upscaled_image
 
     if detailer_hook is not None:
         refined_image = detailer_hook.post_decode(refined_image)
 
     # downscale
-    refined_image = tensor_resize(refined_image, w, h)
+    refined_image = utils.tensor_resize(refined_image, w, h)
 
     # prevent mixing of device
     refined_image = refined_image.cpu()
@@ -457,10 +494,10 @@ def enhance_detail_for_animatediff(image_frames, model, clip, vae, guide_size, g
         image = torch.from_numpy(image).unsqueeze(0)
 
         # upscale
-        upscaled_image = tensor_resize(image, new_w, new_h)
+        upscaled_image = utils.tensor_resize(image, new_w, new_h)
 
         # ksampler
-        samples = to_latent_image(upscaled_image, vae)['samples']
+        samples = utils.to_latent_image(upscaled_image, vae)['samples']
 
         if latent_frames is None:
             latent_frames = samples
@@ -488,11 +525,16 @@ def enhance_detail_for_animatediff(image_frames, model, clip, vae, guide_size, g
         'samples': latent_frames
     }
 
+
+    sampler_opt=None
+    if detailer_hook is not None:
+        sampler_opt = detailer_hook.get_custom_sampler()
+
     if detailer_hook is not None:
         latent = detailer_hook.post_encode(latent)
 
     refined_latent = impact_sampling.ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
-                                                      latent, denoise, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative, scheduler_func=scheduler_func)
+                                                      latent, denoise, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative, scheduler_func=scheduler_func, sampler_opt=sampler_opt)
 
     if detailer_hook is not None:
         refined_latent = detailer_hook.pre_decode(refined_latent)
@@ -580,6 +622,118 @@ class SAMWrapper:
         return sam_predict(predictor, points, plabs, bbox, threshold)
 
 
+class SAM2Wrapper:
+    def __init__(self, config, modelname, is_auto_mode, safe_to_gpu=None, device_mode="AUTO"):
+        self.config = config
+        self.modelname = modelname
+        self.image_predictor = None
+        self.video_predictor = None
+        self.device_mode = device_mode
+        self.safe_to_gpu = safe_to_gpu if safe_to_gpu is not None else SafeToGPU_stub()
+        self.is_auto_mode = is_auto_mode
+
+    def prepare_device(self):
+        pass
+
+    def prepare_image_device(self):
+        if self.is_auto_mode:
+            device = comfy.model_management.get_torch_device()
+            self.safe_to_gpu.to_device(self.image_predictor.model, device=device)
+
+    def prepare_video_device(self):
+        if self.is_auto_mode:
+            device = comfy.model_management.get_torch_device()
+            self.safe_to_gpu.to_device(self.video_predictor, device=device)
+
+    def release_device(self):
+        if self.is_auto_mode:
+            if self.image_predictor:
+                self.image_predictor.model.to(device="cpu")
+            if self.video_predictor:
+                self.video_predictor.to(device="cpu")
+
+    def predict(self, image, points, plabs, bbox, threshold):
+        if self.image_predictor is None:
+            self.image_predictor = SAM2ImagePredictor(build_sam2(self.config, self.modelname))
+
+        self.prepare_image_device()
+
+        self.image_predictor.set_image(image)
+
+        return sam_predict(self.image_predictor, points, plabs, bbox, threshold)
+
+    def predict_video_segs(self, image_frames, segs):
+        if self.video_predictor is None:
+            self.video_predictor = build_sam2_video_predictor(self.config, self.modelname)
+
+        self.prepare_video_device()
+
+        orig_video_height = image_frames.shape[1]
+        orig_video_width = image_frames.shape[2]
+
+        image_frames, padding = utils.resize_with_padding(image_frames, self.video_predictor.image_size, self.video_predictor.image_size)
+        image_frames = image_frames.permute(0, 3, 1, 2)
+
+        inference_state = {}
+        inference_state["images"] = image_frames
+        inference_state["num_frames"] = len(image_frames)
+        inference_state["video_height"] = self.video_predictor.image_size
+        inference_state["video_width"] = self.video_predictor.image_size
+        inference_state["offload_video_to_cpu"] = True
+        inference_state["offload_state_to_cpu"] = self.device_mode == "CPU"
+        inference_state["device"] = self.video_predictor.device
+
+        if inference_state["offload_state_to_cpu"]:
+            inference_state["storage_device"] = torch.device("cpu")
+        else:
+            inference_state["storage_device"] = self.video_predictor.device
+
+        inference_state["point_inputs_per_obj"] = {}
+        inference_state["mask_inputs_per_obj"] = {}
+        inference_state["cached_features"] = {}
+        inference_state["constants"] = {}
+
+        inference_state["obj_id_to_idx"] = OrderedDict()
+        inference_state["obj_idx_to_id"] = OrderedDict()
+        inference_state["obj_ids"] = []
+
+        inference_state["output_dict_per_obj"] = {}
+        inference_state["temp_output_dict_per_obj"] = {}
+        inference_state["frames_tracked_per_obj"] = {}
+        self.video_predictor._get_image_feature(inference_state, frame_idx=0, batch_size=1)
+
+        temp_masks = {}
+        for i in range(0, len(segs[1])):
+            bbox = segs[1][i].bbox
+
+            adjusted_bbox = utils.adjust_bbox_after_resize(
+                bbox,
+                (orig_video_height, orig_video_width),
+                (self.video_predictor.image_size, self.video_predictor.image_size),
+                padding
+            )
+
+            print(f"bbox={bbox} / adjusted_bbox={adjusted_bbox}")
+
+            points = [utils.center_of_bbox(adjusted_bbox)]
+            plabs = [1]
+            self.video_predictor.add_new_points_or_box(inference_state=inference_state, frame_idx=0, obj_id=i, points=points, labels=plabs, box=adjusted_bbox)
+            temp_masks[i] = []
+
+        for frame_idx, object_ids, masks in self.video_predictor.propagate_in_video(inference_state):
+            for i in object_ids:
+                m = masks[i]
+                m = m.permute(1, 2, 0)
+                temp_masks[i].append(m)
+
+        result = {}
+        for k, v in temp_masks.items():
+            m = torch.stack(v, dim=0)
+            m = utils.remove_padding(m, padding)
+            result[k] = utils.resize_with_padding(m, orig_video_width, orig_video_height)[0]
+
+        return result
+
 class ESAMWrapper:
     def __init__(self, model, device):
         self.model = model
@@ -605,10 +759,15 @@ class ESAMWrapper:
 def make_sam_mask(sam, segs, image, detection_hint, dilation,
                   threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
 
-    if not hasattr(sam, 'sam_wrapper'):
+    if not hasattr(sam, 'sam_wrapper') and not isinstance(sam, SAM2Wrapper):
         raise Exception("[Impact Pack] Invalid SAMLoader is connected. Make sure 'SAMLoader (Impact)'.\nKnown issue: The ComfyUI-YOLO node overrides the SAMLoader (Impact), making it unusable. You need to uninstall ComfyUI-YOLO.\n\n\n")
 
-    sam_obj = sam.sam_wrapper
+
+    if isinstance(sam, SAM2Wrapper):
+        sam_obj = sam
+    else:
+        sam_obj = sam.sam_wrapper
+
     sam_obj.prepare_device()
 
     try:
@@ -626,7 +785,7 @@ def make_sam_mask(sam, segs, image, detection_hint, dilation,
 
             for i in range(len(segs)):
                 bbox = segs[i].bbox
-                center = center_of_bbox(segs[i].bbox)
+                center = utils.center_of_bbox(segs[i].bbox)
                 points.append(center)
 
                 # small point is background, big point is foreground
@@ -641,7 +800,7 @@ def make_sam_mask(sam, segs, image, detection_hint, dilation,
         else:
             for i in range(len(segs)):
                 bbox = segs[i].bbox
-                center = center_of_bbox(bbox)
+                center = utils.center_of_bbox(bbox)
 
                 x1 = max(bbox[0] - bbox_expansion, 0)
                 y1 = max(bbox[1] - bbox_expansion, 0)
@@ -687,7 +846,7 @@ def make_sam_mask(sam, segs, image, detection_hint, dilation,
                     plabs = [1, 1, 1, 1]
 
                 elif detection_hint == "mask-point-bbox":
-                    center = center_of_bbox(segs[i].bbox)
+                    center = utils.center_of_bbox(segs[i].bbox)
                     points.append(center)
                     plabs = [1]
 
@@ -708,14 +867,14 @@ def make_sam_mask(sam, segs, image, detection_hint, dilation,
                 total_masks += detected_masks
 
         # merge every collected masks
-        mask = combine_masks2(total_masks)
+        mask = utils.combine_masks2(total_masks)
 
     finally:
         sam_obj.release_device()
 
     if mask is not None:
         mask = mask.float()
-        mask = dilate_mask(mask.cpu().numpy(), dilation)
+        mask = utils.dilate_mask(mask.cpu().numpy(), dilation)
         mask = torch.from_numpy(mask)
     else:
         size = image.shape[0], image.shape[1]
@@ -766,7 +925,7 @@ def generate_detection_hints(image, seg, center, detection_hint, dilated_bbox, m
         plabs = [1, 1, 1, 1]
 
     elif detection_hint == "mask-point-bbox":
-        center = center_of_bbox(seg.bbox)
+        center = utils.center_of_bbox(seg.bbox)
         points.append(center)
         plabs = [1]
 
@@ -856,7 +1015,7 @@ def segs_scale_match(segs, target_shape):
             cropped_mask = cropped_mask.squeeze(0).squeeze(0).numpy()
 
         if cropped_image is not None:
-            cropped_image = tensor_resize(cropped_image if isinstance(cropped_image, torch.Tensor) else torch.from_numpy(cropped_image), new_w, new_h)
+            cropped_image = utils.tensor_resize(cropped_image if isinstance(cropped_image, torch.Tensor) else torch.from_numpy(cropped_image), new_w, new_h)
             cropped_image = cropped_image.numpy()
 
         new_seg = SEG(cropped_image, cropped_mask, seg.confidence, crop_region, bbox, seg.label, seg.control_net_wrapper)
@@ -896,7 +1055,7 @@ def make_sam_mask_segmented(sam, segs, image, detection_hint, dilation,
 
             for i in range(len(segs)):
                 bbox = segs[i].bbox
-                center = center_of_bbox(bbox)
+                center = utils.center_of_bbox(bbox)
                 points.append(center)
 
                 # small point is background, big point is foreground
@@ -911,7 +1070,7 @@ def make_sam_mask_segmented(sam, segs, image, detection_hint, dilation,
         else:
             for i in range(len(segs)):
                 bbox = segs[i].bbox
-                center = center_of_bbox(bbox)
+                center = utils.center_of_bbox(bbox)
                 x1 = max(bbox[0] - bbox_expansion, 0)
                 y1 = max(bbox[1] - bbox_expansion, 0)
                 x2 = min(bbox[2] + bbox_expansion, image.shape[1])
@@ -928,7 +1087,7 @@ def make_sam_mask_segmented(sam, segs, image, detection_hint, dilation,
                 total_masks += detected_masks
 
         # merge every collected masks
-        mask = combine_masks2(total_masks)
+        mask = utils.combine_masks2(total_masks)
 
     finally:
         sam_obj.release_device()
@@ -937,7 +1096,7 @@ def make_sam_mask_segmented(sam, segs, image, detection_hint, dilation,
 
     if mask is not None:
         mask = mask.float()
-        mask = dilate_mask(mask.cpu().numpy(), dilation)
+        mask = utils.dilate_mask(mask.cpu().numpy(), dilation)
         mask = torch.from_numpy(mask)
         mask = mask.to(device=mask_working_device)
     else:
@@ -954,7 +1113,7 @@ def make_sam_mask_segmented(sam, segs, image, detection_hint, dilation,
 
 
 def segs_bitwise_and_mask(segs, mask):
-    mask = make_2d_mask(mask)
+    mask = utils.make_2d_mask(mask)
 
     if mask is None:
         print("[SegsBitwiseAndMask] Cannot operate: MASK is empty.")
@@ -980,7 +1139,7 @@ def segs_bitwise_and_mask(segs, mask):
 
 
 def segs_bitwise_subtract_mask(segs, mask):
-    mask = make_2d_mask(mask)
+    mask = utils.make_2d_mask(mask)
 
     if mask is None:
         print("[SegsBitwiseSubtractMask] Cannot operate: MASK is empty.")
@@ -1036,7 +1195,7 @@ def dilate_segs(segs, factor):
 
     new_segs = []
     for seg in segs[1]:
-        new_mask = dilate_mask(seg.cropped_mask, factor)
+        new_mask = utils.dilate_mask(seg.cropped_mask, factor)
         new_seg = SEG(seg.cropped_image, new_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper)
         new_segs.append(new_seg)
 
@@ -1052,7 +1211,7 @@ class ONNXDetector:
     def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
         drop_size = max(drop_size, 1)
         try:
-            import impact.onnx as onnx
+            import impact.impact_onnx as onnx
 
             h = image.shape[1]
             w = image.shape[2]
@@ -1068,7 +1227,7 @@ class ONNXDetector:
                     x1, y1, x2, y2 = item_bbox
 
                     if x2 - x1 > drop_size and y2 - y1 > drop_size:  # minimum dimension must be (2,2) to avoid squeeze issue
-                        crop_region = make_crop_region(w, h, item_bbox, crop_factor)
+                        crop_region = utils.make_crop_region(w, h, item_bbox, crop_factor)
 
                         if detailer_hook is not None:
                             crop_region = item_bbox.post_crop_region(w, h, item_bbox, crop_region)
@@ -1078,7 +1237,7 @@ class ONNXDetector:
                         # prepare cropped mask
                         cropped_mask = np.zeros((crop_y2 - crop_y1, crop_x2 - crop_x1))
                         cropped_mask[y1 - crop_y1:y2 - crop_y1, x1 - crop_x1:x2 - crop_x1] = 1
-                        cropped_mask = dilate_mask(cropped_mask, dilation)
+                        cropped_mask = utils.dilate_mask(cropped_mask, dilation)
 
                         # make items. just convert the integer label to a string
                         item = SEG(None, cropped_mask, scores[i], crop_region, item_bbox, str(labels[i]), None)
@@ -1153,7 +1312,7 @@ def mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size=1, label='A',
                     np.max(indices[1]),
                     np.max(indices[0]),
                 )
-                crop_region = make_crop_region(
+                crop_region = utils.make_crop_region(
                     mask_i.shape[1], mask_i.shape[0], bbox, crop_factor
                 )
                 x1, y1, x2, y2 = crop_region
@@ -1187,7 +1346,7 @@ def mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size=1, label='A',
 
                 x, y, w, h = cv2.boundingRect(contour)
                 bbox = x, y, x + w, y + h
-                crop_region = make_crop_region(
+                crop_region = utils.make_crop_region(
                     mask_i.shape[1], mask_i.shape[0], bbox, crop_factor, crop_min_size
                 )
 
@@ -1261,7 +1420,7 @@ def mediapipe_facemesh_to_segs(image, crop_factor, bbox_fill, crop_min_size, dro
                 tensor = torch.from_numpy(convex_segment)
                 mask_tensor = torch.any(tensor != 0, dim=-1).float()
                 mask_tensor = mask_tensor.squeeze(0)
-                mask_tensor = torch.from_numpy(dilate_mask(mask_tensor.numpy(), dilation))
+                mask_tensor = torch.from_numpy(utils.dilate_mask(mask_tensor.numpy(), dilation))
                 mask_list.append(mask_tensor.unsqueeze(0))
 
         return mask_list
@@ -1349,9 +1508,14 @@ 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 +1525,14 @@ 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 +1542,12 @@ 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 +1558,15 @@ 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 +1579,15 @@ 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 +1610,16 @@ 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 +1646,7 @@ 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:
@@ -1486,7 +1655,7 @@ class TwoSamplersForMaskUpscaler:
                  hook_full_opt=None,
                  tile_size=512):
 
-        mask = make_2d_mask(mask)
+        mask = utils.make_2d_mask(mask)
 
         mask = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
 
@@ -1504,7 +1673,7 @@ class TwoSamplersForMaskUpscaler:
     def upscale(self, step_info, samples, upscale_factor, save_temp_prefix=None):
         scale_method, sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, vae = self.params
 
-        mask = make_2d_mask(mask)
+        mask = utils.make_2d_mask(mask)
 
         self.prepare_hook(step_info)
 
@@ -1534,7 +1703,7 @@ class TwoSamplersForMaskUpscaler:
     def upscale_shape(self, step_info, samples, w, h, save_temp_prefix=None):
         scale_method, sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, vae = self.params
 
-        mask = make_2d_mask(mask)
+        mask = utils.make_2d_mask(mask)
 
         self.prepare_hook(step_info)
 
@@ -1590,7 +1759,7 @@ class TwoSamplersForMaskUpscaler:
             return cur_step % 2 == 0 or cur_step >= total_step - 1
 
     def do_samples(self, step_info, base_sampler, mask_sampler, sample_schedule, mask, upscaled_latent):
-        mask = make_2d_mask(mask)
+        mask = utils.make_2d_mask(mask)
 
         if self.is_full_sample_time(step_info, sample_schedule):
             print(f"step_info={step_info} / full time")
@@ -1647,8 +1816,14 @@ 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 +1855,9 @@ 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 +1887,9 @@ 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 +2000,14 @@ 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 +2048,17 @@ 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 +2094,7 @@ 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 +2104,7 @@ 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 +2127,14 @@ 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]
@@ -2004,7 +2203,7 @@ class BBoxDetectorBasedOnCLIPSeg:
     def detect(self, image, bbox_threshold, bbox_dilation, bbox_crop_factor, drop_size=1, detailer_hook=None):
         mask = self.detect_combined(image, bbox_threshold, bbox_dilation)
 
-        mask = make_2d_mask(mask)
+        mask = utils.make_2d_mask(mask)
 
         segs = mask_to_segs(mask, False, bbox_crop_factor, True, drop_size, detailer_hook=detailer_hook)
 
@@ -2034,7 +2233,7 @@ class BBoxDetectorBasedOnCLIPSeg:
         prompt = self.aux if self.prompt == '' and self.aux is not None else self.prompt
 
         mask, _, _ = CLIPSeg().segment_image(image, prompt, self.blur, threshold, dilation_factor)
-        mask = to_binary_mask(mask)
+        mask = utils.to_binary_mask(mask)
         return mask
 
     def setAux(self, x):

modules/impact/detectors.py

@@ -1,3 +1,5 @@
+import logging
+
 import impact.core as core
 from nodes import MAX_RESOLUTION
 import impact.segs_nodes as segs_nodes
@@ -163,7 +165,7 @@ class SegmDetectorCombined:
         mask = segm_detector.detect_combined(image, threshold, dilation)
 
         if mask is None:
-            mask = torch.zeros((image.shape[2], image.shape[1]), dtype=torch.float32, device="cpu")
+            mask = torch.zeros((image.shape[1], image.shape[2]), dtype=torch.float32, device="cpu")
 
         return (mask.unsqueeze(0),)
 
@@ -183,7 +185,7 @@ class BboxDetectorCombined(SegmDetectorCombined):
         mask = bbox_detector.detect_combined(image, threshold, dilation)
 
         if mask is None:
-            mask = torch.zeros((image.shape[2], image.shape[1]), dtype=torch.float32, device="cpu")
+            mask = torch.zeros((image.shape[1], image.shape[2]), dtype=torch.float32, device="cpu")
 
         return (mask.unsqueeze(0),)
 
@@ -298,6 +300,71 @@ class SimpleDetectorForEachPipe:
                                             sam_mask_hint_threshold, post_dilation=post_dilation, sam_model_opt=sam_model_opt, segm_detector_opt=segm_detector_opt,
                                             detailer_hook=detailer_hook)
 
+class SAM2VideoDetectorSEGS:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image_frames": ("IMAGE", ),
+
+            "bbox_detector": ("BBOX_DETECTOR", ),
+            "sam2_model": ("SAM_MODEL", ),
+
+            "bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+            "sam2_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+
+            "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
+            "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
+            }
+        }
+
+    RETURN_TYPES = ("SEGS", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Detector"
+
+    @staticmethod
+    def doit(bbox_detector, sam2_model, image_frames, bbox_threshold, sam2_threshold, crop_factor, drop_size):
+        if not isinstance(sam2_model, core.SAM2Wrapper):
+            logging.error("[Impact Pack] To use the SAM2VideoDetectorSEGS node, a SAM2 model must be provided as input to `sam2_model`.")
+            raise Exception("To use the SAM2VideoDetectorSEGS node, a SAM2 model must be provided as input to `sam2_model`.")
+
+        segs = bbox_detector.detect(image_frames[0].unsqueeze(0), bbox_threshold, 0, 0, drop_size)
+        segs_masks = sam2_model.predict_video_segs(image_frames, segs)
+
+        def get_whole_merged_mask(all_masks):
+            merged_mask = (all_masks[0] * 255).to(torch.uint8)
+            for mask in all_masks[1:]:
+                merged_mask |= (mask * 255).to(torch.uint8)
+
+            merged_mask = (merged_mask / 255.0).to(torch.float32)
+            merged_mask = utils.to_binary_mask(merged_mask, 0.1)[0]
+            return merged_mask
+
+        test_mask1 = None
+        test_mask2 = None
+        new_segs = []
+        for k, v in segs_masks.items():
+            v = v.squeeze(3)
+            m = get_whole_merged_mask(v)
+            test_mask2 = v
+            seg = segs_nodes.MaskToSEGS.doit(m, False, crop_factor, False, drop_size, contour_fill=True)[0][1]
+
+            if len(seg) == 0:
+                continue
+
+            seg = seg[0]
+
+            x1, y1, x2, y2 = seg.crop_region
+            masks = []
+            for mask in v:
+                masks.append(mask[y1:y2, x1:x2])
+            cropped_mask = torch.stack(masks)
+            cropped_mask = (cropped_mask >= (sam2_threshold*100-50)).to(torch.uint8).cpu()
+            new_seg = SEG(seg.cropped_image, cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper)
+            new_segs.append(new_seg)
+
+        return ((segs[0], new_segs), )
+
 
 class SimpleDetectorForAnimateDiff:
     @classmethod

modules/impact/hook_nodes.py

@@ -1,6 +1,8 @@
 import sys
 from . import hooks
 from . import defs
+from . import utils
+import nodes
 
 
 class SEGSOrderedFilterDetailerHookProvider:
@@ -83,3 +85,24 @@ class PreviewDetailerHookProvider:
     def doit(self, quality, unique_id):
         hook = hooks.PreviewDetailerHook(unique_id, quality)
         return hook, hook
+
+
+class LamaRemoverDetailerHookProvider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask_threshold":("INT", {"default": 250, "min": 0, "max": 255, "step": 1, "display": "slider"}),
+                "gaussblur_radius": ("INT", {"default": 8, "min": 0, "max": 20, "step": 1, "display": "slider"}),
+                "skip_sampling": ("BOOLEAN", {"default": True}),
+            }
+        }
+
+    RETURN_TYPES = ("DETAILER_HOOK", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, mask_threshold, gaussblur_radius, skip_sampling):
+        hook = hooks.LamaRemoverDetailerHook(mask_threshold, gaussblur_radius, skip_sampling)
+        return (hook, )

modules/impact/hooks.py

@@ -25,7 +25,7 @@ class PixelKSampleHook:
     def post_decode(self, pixels):
         return pixels
 
-    def post_upscale(self, pixels):
+    def post_upscale(self, pixels, mask=None):
         return pixels
 
     def post_encode(self, samples):
@@ -64,8 +64,8 @@ class PixelKSampleHookCombine(PixelKSampleHook):
     def post_decode(self, pixels):
         return self.hook2.post_decode(self.hook1.post_decode(pixels))
 
-    def post_upscale(self, pixels):
-        return self.hook2.post_upscale(self.hook1.post_upscale(pixels))
+    def post_upscale(self, pixels, mask=None):
+        return self.hook2.post_upscale(self.hook1.post_upscale(pixels, mask), mask)
 
     def post_encode(self, samples):
         return self.hook2.post_encode(self.hook1.post_encode(samples))
@@ -109,6 +109,15 @@ class DetailerHookCombine(PixelKSampleHookCombine):
         noise_2nd, is_touched = self.hook2.get_custom_noise(seed, noise, is_touched)
         return noise, is_touched
 
+    def get_custom_sampler(self):
+        if self.hook1.get_custom_sampler() is not None:
+            return self.hook1.get_custom_sampler()
+        else:
+            return self.hook2.get_custom_sampler()
+
+    def get_skip_sampling(self):
+        return self.hook1.get_skip_sampling() and self.hook2.get_skip_sampling()
+
 
 class SimpleCfgScheduleHook(PixelKSampleHook):
     target_cfg = 0
@@ -173,6 +182,21 @@ class DetailerHook(PixelKSampleHook):
     def get_custom_noise(self, seed, noise, is_touched):
         return noise, is_touched
 
+    def get_custom_sampler(self):
+        return None
+
+    def get_skip_sampling(self):
+        return False
+
+
+class CustomSamplerDetailerHookProvider(DetailerHook):
+    def __init__(self, sampler):
+        super().__init__()
+        self.sampler = sampler
+
+    def get_custom_sampler(self):
+        return self.sampler
+
 
 # class CustomNoiseDetailerHookProvider(DetailerHook):
 #     def __init__(self, noise):
@@ -486,6 +510,27 @@ class SEGSLabelFilterDetailerHook(DetailerHook):
         return segs_nodes.SEGSLabelFilter().doit(segs, "", self.labels)[0]
 
 
+class LamaRemoverDetailerHook(DetailerHook):
+    def __init__(self, mask_threshold, gaussblur_radius, skip_sampling):
+        super().__init__()
+        self.mask_threshold = mask_threshold
+        self.gaussblur_radius = gaussblur_radius
+        self.skip_sampling = skip_sampling
+
+    def post_upscale(self, img, mask=None):
+        if "LamaRemover" in nodes.NODE_CLASS_MAPPINGS:
+            lama_remover_obj = nodes.NODE_CLASS_MAPPINGS['LamaRemover']()
+        else:
+            utils.try_install_custom_node('https://github.com/Layer-norm/comfyui-lama-remover',
+                                          "To use 'LAMARemoverDetailerHookProvider', 'comfyui-lama-remover' nodepack is required.")
+            raise Exception("'LamaRemover' node is not installed.")
+
+        return lama_remover_obj.lama_remover(img, masks=mask, mask_threshold=self.mask_threshold, gaussblur_radius=self.gaussblur_radius, invert_mask=False)[0]
+
+    def get_skip_sampling(self):
+        return self.skip_sampling
+
+
 class PreviewDetailerHook(DetailerHook):
     def __init__(self, node_id, quality):
         super().__init__()

modules/impact/impact_pack.py

@@ -28,6 +28,7 @@ import base64
 import impact.wildcards as wildcards
 from . import hooks
 from . import utils
+import inspect
 
 
 try:
@@ -91,13 +92,28 @@ class CLIPSegDetectorProvider:
             print("[ERROR] CLIPSegToBboxDetector: CLIPSeg custom node isn't installed. You must install biegert/ComfyUI-CLIPSeg extension to use this node.")
 
 
+sam2_config_table = {
+    'sam2.1_hiera_base_plus.pt': 'configs/sam2.1/sam2.1_hiera_b+.yaml',
+    'sam2.1_hiera_large.pt': 'configs/sam2.1/sam2.1_hiera_l.yaml',
+    'sam2.1_hiera_small.pt': 'configs/sam2.1/sam2.1_hiera_s.yaml',
+    'sam2.1_hiera_tiny.pt': 'configs/sam2.1/sam2.1_hiera_t.yaml',
+    'sam2_hiera_tiny.pt': 'configs/sam2/sam2_hiera_t.yaml',
+    'sam2_hiera_small.pt': 'configs/sam2/sam2_hiera_s.yaml',
+    'sam2_hiera_base_plus.pt': 'configs/sam2/sam2_hiera_b+.yaml',
+    'sam2_hiera_large.pt': 'configs/sam2/sam2_hiera_l.yaml'
+}
+
 class SAMLoader:
     @classmethod
     def INPUT_TYPES(cls):
-        models = [x for x in folder_paths.get_filename_list("sams") if 'hq' not in x]
+        models = [x for x in folder_paths.get_filename_list("sams") if 'hq' not in x and (x.endswith('.pt') or x.endswith('.pth') or x.endswith('.safetensors'))]
+
+        if 'ESAM_ModelLoader_Zho' in nodes.NODE_CLASS_MAPPINGS:
+            models.append('ESAM')
+
         return {
             "required": {
-                "model_name": (models + ['ESAM'], {"tooltip": "The detection accuracy varies depending on the SAM model. ESAM can only be used if ComfyUI-YoloWorld-EfficientSAM is installed."}),
+                "model_name": (models, {"tooltip": "The detection accuracy varies depending on the SAM model. ESAM can only be used if ComfyUI-YoloWorld-EfficientSAM is installed."}),
                 "device_mode": (["AUTO", "Prefer GPU", "CPU"], {"tooltip": "AUTO: Only applicable when a GPU is available. It temporarily loads the SAM_MODEL into VRAM only when the detection function is used.\n"
                                                                            "Prefer GPU: Tries to keep the SAM_MODEL on the GPU whenever possible. This can be used when there is sufficient VRAM available.\n"
                                                                            "CPU: Always loads only on the CPU."}),
@@ -131,17 +147,22 @@ class SAMLoader:
             
             print(f"Loads EfficientSAM model: (device:{device_mode})")
             return (esam, )
-
-        modelname = folder_paths.get_full_path("sams", model_name)
-
-        if 'vit_h' in model_name:
-            model_kind = 'vit_h'
-        elif 'vit_l' in model_name:
-            model_kind = 'vit_l'
+        elif model_name in sam2_config_table:
+            model_kind = 'sam2'
+            config = sam2_config_table[model_name]
+            modelname = folder_paths.get_full_path("sams", model_name)
         else:
-            model_kind = 'vit_b'
+            modelname = folder_paths.get_full_path("sams", model_name)
+
+            if 'vit_h' in model_name:
+                model_kind = 'vit_h'
+            elif 'vit_l' in model_name:
+                model_kind = 'vit_l'
+            else:
+                model_kind = 'vit_b'
+
+            sam = sam_model_registry[model_kind](checkpoint=modelname)
 
-        sam = sam_model_registry[model_kind](checkpoint=modelname)
         size = os.path.getsize(modelname)
         safe_to = core.SafeToGPU(size)
 
@@ -153,10 +174,14 @@ class SAMLoader:
 
         is_auto_mode = device_mode == "AUTO"
 
-        sam_obj = core.SAMWrapper(sam, is_auto_mode=is_auto_mode, safe_to_gpu=safe_to)
-        sam.sam_wrapper = sam_obj
+        if model_kind == 'sam2':
+            sam = core.SAM2Wrapper(config=config, modelname=modelname, is_auto_mode=is_auto_mode, safe_to_gpu=safe_to, device_mode=device_mode)
+            print(f"Loads SAM2 model: {modelname} (device:{device_mode})")
+        else:
+            sam_obj = core.SAMWrapper(sam, is_auto_mode=is_auto_mode, safe_to_gpu=safe_to)
+            sam.sam_wrapper = sam_obj
+            print(f"Loads SAM model: {modelname} (device:{device_mode})")
 
-        print(f"Loads SAM model: {modelname} (device:{device_mode})")
         return (sam, )
 
 
@@ -191,7 +216,7 @@ 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 +242,8 @@ 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 +252,17 @@ class DetailerForEach:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = "It enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size."
+
+    @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 +298,7 @@ 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 +326,16 @@ 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 +355,22 @@ 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 +380,7 @@ 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 +398,7 @@ 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 +413,15 @@ 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 +444,7 @@ 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 +456,8 @@ 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"}),
                      }
                 }
 
@@ -422,10 +468,13 @@ class DetailerForEachPipe:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = DetailerForEach.DESCRIPTION
+
     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 +492,8 @@ 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 +507,7 @@ 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 +550,8 @@ 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")
@@ -509,6 +561,8 @@ class FaceDetailer:
 
     CATEGORY = "ImpactPack/Simple"
 
+    DESCRIPTION = "This node enhances details by automatically detecting specific objects in the input image using detection models (bbox, segm, sam) and regenerating the image by enlarging the detected area based on the guide size.\nAlthough this node is specialized to simplify the commonly used facial detail enhancement workflow, it can also be used for various automatic inpainting purposes depending on the detection model."
+
     @staticmethod
     def enhance_face(image, 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,
@@ -517,7 +571,7 @@ 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 +603,8 @@ 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 +630,8 @@ 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 +649,8 @@ 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
@@ -774,6 +831,26 @@ class CoreMLDetailerHookProvider:
         return (hook, )
 
 
+class CustomSamplerDetailerHookProvider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "sampler": ("SAMPLER", ),
+                    },
+                }
+
+    RETURN_TYPES = ("DETAILER_HOOK",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Detailer"
+    
+    DESCRIPTION = "Apply a hook that allows you to use a custom sampler in the Detailer nodes. When using `DetailerHookCombine`, the sampler from the first hook is applied."
+
+    def doit(self, sampler):
+        hook = hooks.CustomSamplerDetailerHookProvider(sampler)
+        return (hook, )
+
+
 class CfgScheduleHookProvider:
     schedules = ["simple"]
 
@@ -981,6 +1058,7 @@ 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 +1068,11 @@ 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 +1327,7 @@ 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 +1342,8 @@ 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 +1355,7 @@ class IterativeLatentUpscale:
 
         core.update_node_status(unique_id, "", None)
 
-        return (current_latent, upscaler.vae)
+        return current_latent, upscaler.vae
 
 
 class IterativeImageUpscale:
@@ -1304,7 +1385,11 @@ 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 +1411,7 @@ 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 +1445,8 @@ 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"}),
                    }
                 }
 
@@ -1370,11 +1457,14 @@ class FaceDetailerPipe:
 
     CATEGORY = "ImpactPack/Simple"
 
+    DESCRIPTION = FaceDetailer.DESCRIPTION
+
     def doit(self, image, detailer_pipe, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
              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 +1487,8 @@ 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
@@ -1463,6 +1554,8 @@ class MaskDetailerPipe:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = ""
+
     def doit(self, image, mask, basic_pipe, guide_size, guide_size_for, max_size, mask_mode,
              seed, steps, cfg, sampler_name, scheduler, denoise,
              feather, crop_factor, drop_size, refiner_ratio, batch_size, cycle=1,
@@ -1531,7 +1624,7 @@ 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 +1633,8 @@ 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:
@@ -1567,9 +1661,12 @@ class DetailerForEachTestPipe(DetailerForEachPipe):
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = DetailerForEach.DESCRIPTION
+
     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 +1685,8 @@ 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 +1924,135 @@ 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 str(node["id"]) == 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 +2250,12 @@ 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 +2297,33 @@ 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)
@@ -2146,17 +2397,25 @@ 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": (["populate", "fixed", "reproduce"], {"default": "populate", "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.\n"
+                            "reproduce: This mode operates as 'fixed' mode only once for reproduction, and then it switches to 'populate' 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", )
+    RETURN_NAMES = ("processed text",)
     FUNCTION = "doit"
 
     @staticmethod
@@ -2174,17 +2433,24 @@ 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": (["populate", "fixed", "reproduce"], {"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\n."
+                            "reproduce: This mode operates as 'fixed' mode only once for reproduction, and then it switches to 'populate' 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 +2475,7 @@ 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]', 'OSS FLUX', 'OSS Wan'],),
         }}
 
     CATEGORY = "ImpactPack/Util"

modules/impact/impact_sampling.py

@@ -27,6 +27,10 @@ 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]
+    elif scheduler.startswith('OSS'):
+        sigmas = nodes.NODE_CLASS_MAPPINGS['OptimalStepsScheduler']().get_sigmas(scheduler[4:], steps, denoise=1.0)[0]
     else:
         sigmas = samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, steps)
 
@@ -44,65 +48,27 @@ def get_noise_sampler(x, cpu, total_sigmas, **kwargs):
 
 
 def ksampler(sampler_name, total_sigmas, extra_options={}, inpaint_options={}):
-    if sampler_name == "dpmpp_sde":
-        def sample_dpmpp_sde(model, x, sigmas, **kwargs):
-            noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
-            if noise_sampler is not None:
-                kwargs['noise_sampler'] = noise_sampler
+    if sampler_name in ["dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu"]:
+        if sampler_name == "dpmpp_sde":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_sde
+        elif sampler_name == "dpmpp_sde_gpu":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_sde_gpu
+        elif sampler_name == "dpmpp_2m_sde":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_2m_sde
+        elif sampler_name == "dpmpp_2m_sde_gpu":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_2m_sde_gpu
+        elif sampler_name == "dpmpp_3m_sde":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_3m_sde
+        elif sampler_name == "dpmpp_3m_sde_gpu":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_3m_sde_gpu
 
-            return k_diffusion_sampling.sample_dpmpp_sde(model, x, sigmas, **kwargs)
+        def sampler_function_wrapper(model, x, sigmas, **kwargs):
+            if 'noise_sampler' not in kwargs:
+                kwargs['noise_sampler'] = get_noise_sampler(x, 'gpu' not in sampler_name, total_sigmas, **kwargs)
 
-        sampler_function = sample_dpmpp_sde
+            return orig_sampler_function(model, x, sigmas, **kwargs)
 
-    elif sampler_name == "dpmpp_sde_gpu":
-        def sample_dpmpp_sde(model, x, sigmas, **kwargs):
-            noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
-            if noise_sampler is not None:
-                kwargs['noise_sampler'] = noise_sampler
-
-            return k_diffusion_sampling.sample_dpmpp_sde_gpu(model, x, sigmas, **kwargs)
-
-        sampler_function = sample_dpmpp_sde
-
-    elif sampler_name == "dpmpp_2m_sde":
-        def sample_dpmpp_sde(model, x, sigmas, **kwargs):
-            noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
-            if noise_sampler is not None:
-                kwargs['noise_sampler'] = noise_sampler
-
-            return k_diffusion_sampling.sample_dpmpp_2m_sde(model, x, sigmas, **kwargs)
-
-        sampler_function = sample_dpmpp_sde
-
-    elif sampler_name == "dpmpp_2m_sde_gpu":
-        def sample_dpmpp_sde(model, x, sigmas, **kwargs):
-            noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
-            if noise_sampler is not None:
-                kwargs['noise_sampler'] = noise_sampler
-
-            return k_diffusion_sampling.sample_dpmpp_2m_sde_gpu(model, x, sigmas, **kwargs)
-
-        sampler_function = sample_dpmpp_sde
-
-    elif sampler_name == "dpmpp_3m_sde":
-        def sample_dpmpp_sde(model, x, sigmas, **kwargs):
-            noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
-            if noise_sampler is not None:
-                kwargs['noise_sampler'] = noise_sampler
-
-            return k_diffusion_sampling.sample_dpmpp_3m_sde(model, x, sigmas, **kwargs)
-
-        sampler_function = sample_dpmpp_sde
-
-    elif sampler_name == "dpmpp_3m_sde_gpu":
-        def sample_dpmpp_sde(model, x, sigmas, **kwargs):
-            noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
-            if noise_sampler is not None:
-                kwargs['noise_sampler'] = noise_sampler
-
-            return k_diffusion_sampling.sample_dpmpp_3m_sde_gpu(model, x, sigmas, **kwargs)
-
-        sampler_function = sample_dpmpp_sde
+        sampler_function = sampler_function_wrapper
 
     else:
         return comfy.samplers.sampler_object(sampler_name)
@@ -228,7 +194,7 @@ def impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, ne
 
 
 def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise,
-                     refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, sigma_factor=1.0, noise=None, scheduler_func=None):
+                     refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, sigma_factor=1.0, noise=None, scheduler_func=None, sampler_opt=None):
 
     if refiner_ratio is None or refiner_model is None or refiner_clip is None or refiner_positive is None or refiner_negative is None:
         # Use separated_sample instead of KSampler for `AYS scheduler`
@@ -240,7 +206,7 @@ def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive,
 
         refined_latent = separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler,
                                           positive, negative, latent_image, start_at_step, end_at_step, False,
-                                          sigma_ratio=sigma_factor, noise=noise, scheduler_func=scheduler_func)
+                                          sigma_ratio=sigma_factor, sampler_opt=sampler_opt, noise=noise, scheduler_func=scheduler_func)
     else:
         advanced_steps = math.floor(steps / denoise)
         start_at_step = advanced_steps - steps
@@ -249,7 +215,7 @@ def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive,
         # print(f"pre: {start_at_step} .. {end_at_step} / {advanced_steps}")
         temp_latent = separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler,
                                        positive, negative, latent_image, start_at_step, end_at_step, True,
-                                       sigma_ratio=sigma_factor, noise=noise, scheduler_func=scheduler_func)
+                                       sigma_ratio=sigma_factor, sampler_opt=sampler_opt, noise=noise, scheduler_func=scheduler_func)
 
         if 'noise_mask' in latent_image:
             # noise_latent = \
@@ -263,7 +229,7 @@ def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive,
         # print(f"post: {end_at_step} .. {advanced_steps + 1} / {advanced_steps}")
         refined_latent = separated_sample(refiner_model, False, seed, advanced_steps, cfg, sampler_name, scheduler,
                                           refiner_positive, refiner_negative, temp_latent, end_at_step, advanced_steps + 1, False,
-                                          sigma_ratio=sigma_factor, scheduler_func=scheduler_func)
+                                          sigma_ratio=sigma_factor, sampler_opt=sampler_opt, scheduler_func=scheduler_func)
 
     return refined_latent
 

modules/impact/impact_server.py

@@ -17,42 +17,11 @@ import numpy as np
 import nodes
 from PIL import Image
 import io
-import impact.wildcards as wildcards
 import comfy
 from io import BytesIO
 import random
 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
@@ -108,9 +77,13 @@ async def sam_prepare(request):
         if data['sam_model_name'] == 'auto':
             model_name = impact.config.get_config()['sam_editor_model']
 
-        model_name = os.path.join(impact_pack.model_path, "sams", model_name)
+        model_path = folder_paths.get_full_path("sams", model_name)
 
-        print(f"[INFO] ComfyUI-Impact-Pack: Loading SAM model '{impact_pack.model_path}'")
+        if model_path is None:
+            logging.error(f"[Impact Pack] The '{model_name}' model file cannot be found in any sams model path.")
+            return web.Response(status=400)
+
+        logging.info(f"[Impact Pack] Loading SAM model '{model_path}'")
 
         filename, image_dir = folder_paths.annotated_filepath(data["filename"])
 
@@ -123,10 +96,10 @@ async def sam_prepare(request):
         if image_dir is None:
             return web.Response(status=400)
 
-        thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_name, filename,))
+        thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_path, filename,))
         thread.start()
 
-        print(f"[INFO] ComfyUI-Impact-Pack: SAM model loaded. ")
+        logging.info("[Impact Pack] SAM model loaded. ")
     return web.Response(status=200)
 
 
@@ -138,7 +111,7 @@ 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")
@@ -209,7 +182,7 @@ async def wildcards_list(request):
 @PromptServer.instance.routes.post("/impact/wildcards")
 async def populate_wildcards(request):
     data = await request.json()
-    populated = wildcards.process(data['text'], data.get('seed', None))
+    populated = impact.wildcards.process(data['text'], data.get('seed', None))
     return web.json_response({"text": populated})
 
 
@@ -346,6 +319,8 @@ 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 +328,24 @@ 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 +354,14 @@ 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 +385,11 @@ def onprompt_for_switch(json_data):
                 if vv[0] in inversed_switch_info:
                     if vv[1] + 1 != inversed_switch_info[vv[0]]:
                         disable_targets.add(kk)
+                    else:
+                        del inversed_switch_info[k]
+
+                if vv[0] in disabled_switch:
+                    disable_targets.add(kk)
 
         if k in onprompt_switch_info:
             selected_slot_name = f"input{onprompt_switch_info[k]}"
@@ -418,6 +406,11 @@ 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 +433,14 @@ def gc_preview_bridge_cache(json_data):
 
     for key in list(core.preview_bridge_cache.keys()):
         if key not in prompt_keys:
-            print(f"key deleted: {key}")
+            # print(f"key deleted [PB]: {key}")
             del core.preview_bridge_cache[key]
 
+    for key in list(core.preview_bridge_last_mask_cache.keys()):
+        if key not in prompt_keys:
+            # print(f"key deleted [PB_last_mask]: {key}")
+            del core.preview_bridge_last_mask_cache[key]
+
 
 def workflow_imagereceiver_update(json_data):
     prompt = json_data['prompt']
@@ -483,7 +481,17 @@ def onprompt_populate_wildcards(json_data):
     for k, v in prompt.items():
         if 'class_type' in v and (v['class_type'] == 'ImpactWildcardEncode' or v['class_type'] == 'ImpactWildcardProcessor'):
             inputs = v['inputs']
-            if inputs['mode'] and isinstance(inputs['populated_text'], str):
+
+            # legacy adapter
+            if isinstance(inputs['mode'], bool):
+                if inputs['mode']:
+                    new_mode = 'populate'
+                else:
+                    new_mode = 'fixed'
+
+                inputs['mode'] = new_mode
+
+            if inputs['mode'] == 'populate' and isinstance(inputs['populated_text'], str):
                 if isinstance(inputs['seed'], list):
                     try:
                         input_node = prompt[inputs['seed'][0]]
@@ -496,25 +504,30 @@ 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
                 else:
                     input_seed = int(inputs['seed'])
 
-                inputs['populated_text'] = wildcards.process(inputs['wildcard_text'], input_seed)
-                inputs['mode'] = False
+                inputs['populated_text'] = impact.wildcards.process(inputs['wildcard_text'], input_seed)
+                inputs['mode'] = 'reproduce'
 
                 PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "populated_text", "type": "STRING", "value": inputs['populated_text']})
                 updated_widget_values[k] = inputs['populated_text']
+            
+            if inputs['mode'] == 'reproduce':
+                PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "mode", "type": "STRING", "value": 'populate'})
+
+
 
     if 'extra_data' in json_data and 'extra_pnginfo' in json_data['extra_data']:
         for node in json_data['extra_data']['extra_pnginfo']['workflow']['nodes']:
             key = str(node['id'])
             if key in updated_widget_values:
                 node['widgets_values'][1] = updated_widget_values[key]
-                node['widgets_values'][2] = False
+                node['widgets_values'][2] = 'reproduce'
 
 
 def onprompt_for_remote(json_data):
@@ -559,7 +572,7 @@ 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
 

modules/impact/logics.py

@@ -8,7 +8,7 @@ from impact.utils import any_typ
 import impact.core as core
 import re
 import nodes
-import traceback
+
 
 class ImpactCompare:
     @classmethod
@@ -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):
@@ -556,27 +574,6 @@ class ImpactSleep:
         return (signal,)
 
 
-error_skip_flag = False
-try:
-    import cm_global
-    def filter_message(str):
-        global error_skip_flag
-
-        if "IMPACT-PACK-SIGNAL: STOP CONTROL BRIDGE" in str:
-            return True
-        elif error_skip_flag and "ERROR:root:!!! Exception during processing !!!\n" == str:
-            error_skip_flag = False
-            return True
-        else:
-            return False
-
-    cm_global.try_call(api='cm.register_message_collapse', f=filter_message)
-
-except Exception as e:
-    print(f"[WARN] ComfyUI-Impact-Pack: `ComfyUI` or `ComfyUI-Manager` is an outdated version.")
-    pass
-
-
 def workflow_to_map(workflow):
     nodes = {}
     links = {}
@@ -673,7 +670,7 @@ 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.")
@@ -683,6 +680,9 @@ class ImpactControlBridge:
                 return (value, )
             else:
                 return (ExecutionBlocker(None), )
+        elif extra_pnginfo is None:
+            logging.warn(f"[Impact Pack] limitation: '{behavior}' behavior cannot be used in API execution.")
+            return (value,)
         else:
             workflow_nodes, links = workflow_to_map(extra_pnginfo['workflow'])
 
@@ -748,6 +748,29 @@ 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
 
 

modules/impact/pipe.py

@@ -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):

modules/impact/sample_error_enhancer.py

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

modules/impact/segs_nodes.py

@@ -13,6 +13,7 @@ from . import segs_upscaler
 from comfy.cli_args import args
 import math
 
+from typing import Callable, Union
 
 try:
     from comfy_extras import nodes_differential_diffusion
@@ -38,7 +39,7 @@ 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}),
 
@@ -60,6 +61,8 @@ class SEGSDetailer:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is applied specifically to SEGS rather than the entire image. To apply it to the entire image, use the 'SEGS Paste' node."
+
     @staticmethod
     def do_detail(image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
                   denoise, noise_mask, force_inpaint, basic_pipe, refiner_ratio=None, batch_size=1, cycle=1,
@@ -76,7 +79,7 @@ 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 +116,17 @@ 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)
@@ -170,6 +177,8 @@ class SEGSPaste:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = "This node provides a function to paste the enhanced SEGS, improved through the SEGS detailer, back onto the original image."
+
     @staticmethod
     def doit(image, segs, feather, alpha=255, ref_image_opt=None):
 
@@ -496,7 +505,7 @@ class SEGSOrderedFilter:
     def INPUT_TYPES(s):
         return {"required": {
                         "segs": ("SEGS", ),
-                        "target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2", "confidence"],),
+                        "target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2", "confidence", "none"],),
                         "order": ("BOOLEAN", {"default": True, "label_on": "descending", "label_off": "ascending"}),
                         "take_start": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
                         "take_count": ("INT", {"default": 1, "min": 0, "max": sys.maxsize, "step": 1}),
@@ -509,51 +518,35 @@ class SEGSOrderedFilter:
 
     CATEGORY = "ImpactPack/Util"
 
+    @staticmethod
+    def get_sort_key_fn(target: str) -> Union[Callable, None]:
+        if target == "none":
+            return None
+                
+        def sort_key_fn(seg):
+            x1, y1, x2, y2 = seg.crop_region
+            if target == "confidence": return seg.confidence
+            if target == "area(=w*h)": return (x2 - x1) * (y2 - y1)
+            if target == "width": return x2 - x1
+            if target == "height": return y2 - y1
+            if target == "x1": return x1
+            if target == "y1": return y1
+            if target == "x2": return x2
+            if target == "y2": return y2
+            raise Exception(f"[Impact Pack] SEGSOrderedFilter - Unexpected target '{target}'")
+                
+        return sort_key_fn
+
     def doit(self, segs, target, order, take_start, take_count):
-        segs_with_order = []
+        sort_key_fn = SEGSOrderedFilter.get_sort_key_fn(target)
 
-        for seg in segs[1]:
-            x1 = seg.crop_region[0]
-            y1 = seg.crop_region[1]
-            x2 = seg.crop_region[2]
-            y2 = seg.crop_region[3]
+        sorted_list = list(segs[1]) # make a shallow copy, so it does not mutate the original list when sort
+        if sort_key_fn is not None:
+            sorted_list.sort(key=sort_key_fn, reverse=order)
 
-            if target == "area(=w*h)":
-                value = (y2 - y1) * (x2 - x1)
-            elif target == "width":
-                value = x2 - x1
-            elif target == "height":
-                value = y2 - y1
-            elif target == "x1":
-                value = x1
-            elif target == "x2":
-                value = x2
-            elif target == "y1":
-                value = y1
-            elif target == "y2":
-                value = y2
-            elif target == "confidence":
-                value = seg.confidence
-            else:
-                raise Exception(f"[Impact Pack] SEGSOrderedFilter - Unexpected target '{target}'")
-
-            segs_with_order.append((value, seg))
-
-        if order:
-            sorted_list = sorted(segs_with_order, key=lambda x: x[0], reverse=True)
-        else:
-            sorted_list = sorted(segs_with_order, key=lambda x: x[0], reverse=False)
-
-        result_list = []
-        remained_list = []
-
-        for i, item in enumerate(sorted_list):
-            if take_start <= i < take_start + take_count:
-                result_list.append(item[1])
-            else:
-                remained_list.append(item[1])
-
-        return (segs[0], result_list), (segs[0], remained_list),
+        take_stop = take_start + take_count
+        return (segs[0], sorted_list[take_start:take_stop]), \
+            (segs[0], sorted_list[:take_start] + sorted_list[take_stop:]),
 
 
 class SEGSRangeFilter:
@@ -621,6 +614,111 @@ class SEGSRangeFilter:
         return (segs[0], new_segs), (segs[0], remained_segs),
 
 
+class SEGSIntersectionFilter:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "segs1": ("SEGS", ),
+                        "segs2": ("SEGS", ),
+                        "ioa_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                     },
+                }
+
+    RETURN_TYPES = ("SEGS",)
+    RETURN_NAMES = ("filtered_SEGS",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def compute_ioa(self, mask1, mask2):
+        """Compute Intersection over Area (IoA) between two boxes."""
+        inter_mask = utils.bitwise_and_masks(mask1, mask2)
+        
+        inter_area = (inter_mask > 0).sum()
+        area1 = (mask1 > 0).sum()
+
+        return inter_area / area1 if area1 > 0 else 0
+
+    def doit(self, segs1, segs2, ioa_threshold):
+        """Remove segments from segs1 if their IoA with any segment in segs2 exceeds the threshold."""
+        # Extract bounding boxes for all segments in segs1 and segs2
+        keep = []
+
+        # Iterate over all segments in segs1
+        for idx1, seg1 in enumerate(segs1[1]):
+            keep_segment = True  # Assume the segment should be kept
+            mask1 = core.segs_to_combined_mask((segs1[0], [seg1]))
+
+            # Compare with every segment in segs2
+            for seg2 in segs2[1]:
+                mask2 = core.segs_to_combined_mask((segs2[0], [seg2]))
+                ioa = self.compute_ioa(mask1, mask2)  # IoA between segment 1 and segment 2
+                
+                if ioa > ioa_threshold:  # If IoA exceeds the threshold, mark the segment for removal
+                    keep_segment = False
+                    break  # If one overlap exceeds threshold, break early and mark for removal
+
+            # Keep the segment if it did not exceed the threshold with any other segment
+            if keep_segment:
+                keep.append(segs1[1][idx1])
+
+        return (segs1[0], keep),  # Return the updated SEGS
+
+
+class SEGSNMSFilter:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "segs": ("SEGS",),
+                "iou_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+            },
+        }
+
+    RETURN_TYPES = ("SEGS",)
+    RETURN_NAMES = ("filtered_SEGS",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def compute_iou(self, mask1, mask2):
+        """Compute IoU between two bounding boxes (x1, y1, x2, y2)."""
+        inter_mask = utils.bitwise_and_masks(mask1, mask2)
+        union_mask = utils.add_masks(mask1, mask2)
+        
+        inter_area = (inter_mask > 0).sum()
+        union_area = (union_mask > 0).sum()
+
+        return inter_area / union_area if union_area > 0 else 0
+
+    def doit(self, segs, iou_threshold):
+        """Perform NMS to filter overlapping segments."""
+        confidences = np.ndarray.flatten(np.array([seg.confidence for seg in segs[1]]))
+
+        # Sort boxes by confidence (high to low)
+        sorted_indices = np.argsort(confidences)[::-1].tolist()
+        keep = []
+
+        while len(sorted_indices) > 0:
+            idx = sorted_indices[0]
+            mask1 = core.segs_to_combined_mask((segs[0], [segs[1][idx]]))
+            keep.append(idx)
+            sorted_indices = sorted_indices[1:]
+
+            # Filter indices only contain the indices where the bbox does not intersect
+            filtered_indices = []
+            for i in sorted_indices:
+                mask2 = core.segs_to_combined_mask((segs[0], [segs[1][i]]))
+                iou = self.compute_iou(mask1, mask2)
+                if iou < iou_threshold:
+                    filtered_indices.append(i)
+
+            sorted_indices = np.array(filtered_indices)
+
+        filtered_segs = [segs[1][i] for i in keep]
+        return (segs[0], filtered_segs),
+
+
 class SEGSToImageList:
     @classmethod
     def INPUT_TYPES(s):
@@ -704,6 +802,68 @@ 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 +994,7 @@ 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 +1199,10 @@ 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 +1460,8 @@ class ControlNetApplySEGS:
     RETURN_TYPES = ("SEGS",)
     FUNCTION = "doit"
 
+    DEPRECATED = True
+
     CATEGORY = "ImpactPack/Util"
 
     @staticmethod
@@ -1327,7 +1489,8 @@ class ControlNetApplyAdvancedSEGS:
                     },
                 "optional": {
                     "segs_preprocessor": ("SEGS_PREPROCESSOR",),
-                    "control_image": ("IMAGE",)
+                    "control_image": ("IMAGE",),
+                    "vae": ("VAE",)
                     }
                 }
 
@@ -1337,13 +1500,13 @@ 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)
 
@@ -1413,12 +1576,12 @@ class SEGSPicker:
 
     RETURN_TYPES = ("SEGS", )
 
-    OUTPUT_NODE = True
-
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Util"
 
+    DESCRIPTION = "This node provides a function to select only the chosen SEGS from the input SEGS."
+
     @staticmethod
     def doit(picks, segs, fallback_image_opt=None, unique_id=None):
         if fallback_image_opt is not None:
@@ -1475,6 +1638,8 @@ class DefaultImageForSEGS:
 
     CATEGORY = "ImpactPack/Util"
 
+    DESCRIPTION = "If the SEGS have not passed through the detailer, they contain only detection area information without an image. This node sets a default image for the SEGS."
+
     @staticmethod
     def doit(segs, image, override):
         results = []

modules/impact/segs_upscaler.py

@@ -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:

modules/impact/util_nodes.py

@@ -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):
@@ -412,6 +446,31 @@ class MakeMaskList:
         return (masks, )
 
 
+class NthItemOfAnyList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":  {
+                    "any_list": (any_typ,),
+                    "index": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1, "tooltip": "The index of the item you want to select from the list."}),
+                    }
+        }
+
+    RETURN_TYPES = (any_typ,)
+    INPUT_IS_LIST = True
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    DESCRIPTION = "Selects the Nth item from a list. If the index is out of range, it returns the last item in the list."
+
+    def doit(self, any_list, index):
+        i = index[0]
+        if i >= len(any_list):
+            return (any_list[-1],)
+        else:
+            return (any_list[i],)
+
+
 class MakeImageList:
     @classmethod
     def INPUT_TYPES(s):
@@ -470,7 +529,7 @@ 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 +551,9 @@ 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 +561,22 @@ 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)
 

modules/impact/utils.py

@@ -7,6 +7,7 @@ import nodes
 from . import config
 from PIL import Image
 import comfy
+import time
 
 
 class TensorBatchBuilder:
@@ -66,6 +67,54 @@ def tensor_convert_rgb(image, prefer_copy=True):
     raise ValueError(f"illegal conversion (channels: {n_channel} -> 3)")
 
 
+def resize_with_padding(image, target_w: int, target_h: int):
+    _tensor_check_image(image)
+    b, h, w, c = image.shape
+    image = image.permute(0, 3, 1, 2)  # B, C, H, W
+
+    scale = min(target_w / w, target_h / h)
+    new_w, new_h = int(w * scale), int(h * scale)
+
+    image = F.interpolate(image, size=(new_h, new_w), mode="bilinear", align_corners=False)
+
+    pad_left = (target_w - new_w) // 2
+    pad_right = target_w - new_w - pad_left
+    pad_top = (target_h - new_h) // 2
+    pad_bottom = target_h - new_h - pad_top
+
+    image = F.pad(image, (pad_left, pad_right, pad_top, pad_bottom), mode='constant', value=0)
+
+    image = image.permute(0, 2, 3, 1)  # B, H, W, C
+    return image, (pad_top, pad_bottom, pad_left, pad_right)
+
+
+def remove_padding(image, padding):
+    pad_top, pad_bottom, pad_left, pad_right = padding
+    return image[:, pad_top:image.shape[1] - pad_bottom, pad_left:image.shape[2] - pad_right, :]
+
+
+def adjust_bbox_after_resize(bbox, original_size, target_size, padding):
+    """
+    bbox: (x1, y1, x2, y2) in original image
+    original_size: (original_h, original_w)
+    target_size: (target_h, target_w)
+    padding: (pad_top, pad_bottom, pad_left, pad_right)
+    """
+    orig_h, orig_w = original_size
+    target_h, target_w = target_size
+    pad_top, pad_bottom, pad_left, pad_right = padding
+
+    scale = min(target_w / orig_w, target_h / orig_h)
+
+    # Apply scale
+    x1 = int(bbox[0] * scale + pad_left)
+    y1 = int(bbox[1] * scale + pad_top)
+    x2 = int(bbox[2] * scale + pad_left)
+    y2 = int(bbox[3] * scale + pad_top)
+
+    return x1, y1, x2, y2
+
+
 def general_tensor_resize(image, w: int, h: int):
     _tensor_check_image(image)
     image = image.permute(0, 3, 1, 2)
@@ -177,31 +226,71 @@ def tensor2numpy(image):
 
 
 def tensor_paste(image1, image2, left_top, mask):
-    """Mask and image2 has to be the same size"""
+    """
+    Pastes image2 onto image1 at position left_top using mask.
+    Supports both RGB and RGBA images.
+    """
     _tensor_check_image(image1)
     _tensor_check_image(image2)
     _tensor_check_mask(mask)
+    
     if image2.shape[1:3] != mask.shape[1:3]:
         mask = resize_mask(mask.squeeze(dim=3), image2.shape[1:3]).unsqueeze(dim=3)
-        # raise ValueError(f"Inconsistent size: Image ({image2.shape[1:3]}) != Mask ({mask.shape[1:3]})")
-
+    
     x, y = left_top
-    _, h1, w1, _ = image1.shape
-    _, h2, w2, _ = image2.shape
-
-    # calculate image patch size
+    _, h1, w1, c1 = image1.shape
+    _, h2, w2, c2 = image2.shape
+    
+    # Calculate image patch size
     w = min(w1, x + w2) - x
     h = min(h1, y + h2) - y
-
+    
     # If the patch is out of bound, nothing to do!
     if w <= 0 or h <= 0:
         return
-
+    
     mask = mask[:, :h, :w, :]
-    image1[:, y:y+h, x:x+w, :] = (
-        (1 - mask) * image1[:, y:y+h, x:x+w, :] +
-        mask * image2[:, :h, :w, :]
-    )
+    
+    # Get the region to be modified
+    region1 = image1[:, y:y+h, x:x+w, :]
+    region2 = image2[:, :h, :w, :]
+    
+    # Handle RGB and RGBA cases
+    if c1 == 3 and c2 == 3:
+        # Both RGB - simple case
+        image1[:, y:y+h, x:x+w, :] = (1 - mask) * region1 + mask * region2
+    
+    elif c1 == 4 and c2 == 4:
+        # Both RGBA - need to handle alpha channel separately
+        # RGB channels
+        image1[:, y:y+h, x:x+w, :3] = (
+            (1 - mask) * region1[:, :, :, :3] +
+            mask * region2[:, :, :, :3]
+        )
+        
+        # Alpha channel - use "over" composition
+        a1 = region1[:, :, :, 3:4]
+        a2 = region2[:, :, :, 3:4] * mask
+        new_alpha = a1 + a2 * (1 - a1)
+        image1[:, y:y+h, x:x+w, 3:4] = new_alpha
+    
+    elif c1 == 4 and c2 == 3:
+        # Target is RGBA, source is RGB - assume source is fully opaque
+        image1[:, y:y+h, x:x+w, :3] = (
+            (1 - mask) * region1[:, :, :, :3] +
+            mask * region2
+        )
+        # Alpha channel - reduce alpha where mask is applied
+        image1[:, y:y+h, x:x+w, 3:4] = region1[:, :, :, 3:4] * (1 - mask) + mask
+    
+    elif c1 == 3 and c2 == 4:
+        # Target is RGB, source is RGBA - apply source alpha to mask
+        effective_mask = mask * region2[:, :, :, 3:4]
+        image1[:, y:y+h, x:x+w, :] = (
+            (1 - effective_mask) * region1 +
+            effective_mask * region2[:, :, :, :3]
+        )
+    
     return
 
 
@@ -501,15 +590,21 @@ 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):

modules/impact/wildcards.py

@@ -8,11 +8,12 @@ import numpy as np
 import threading
 from impact import utils
 from impact import config
+import logging
 
 
 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 = {}
 
@@ -44,7 +45,9 @@ def read_wildcard(k, v):
     elif isinstance(v, str):
         k = wildcard_normalize(k)
         wildcard_dict[k] = [v]
-
+    elif isinstance(v, (int, float)):
+        k = wildcard_normalize(k)
+        wildcard_dict[k] = [str(v)]
 
 def read_wildcard_dict(wildcard_path):
     global wildcard_dict
@@ -58,12 +61,12 @@ 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
-            elif file.endswith('.yaml'):
+                        wildcard_dict[key] = [x for x in lines if not x.strip().startswith('#')]
+            elif file.endswith('.yaml') or file.endswith('.yml'):
                 file_path = os.path.join(root, file)
 
                 try:
@@ -121,7 +124,7 @@ 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])
@@ -135,6 +138,8 @@ def process(text, seed=None):
                     b = r.group(3)
                     if b is not None:
                         b = b.strip()
+                    else:
+                        b = "-1"
                         
                 if r is not None:
                     if b is not None and is_numeric_string(a) and is_numeric_string(b):
@@ -145,26 +150,32 @@ def process(text, seed=None):
                         x = int(a)
                         select_range = (x, x)
 
+                    # Expand wildcard path or return the string after $$
+                    def expand_wildcard_or_return_string(options, pattern, wildcard_pattern):
+                        matches = re.findall(wildcard_pattern, pattern)
+                        if len(options) == 1 and matches:
+                            # $$<single wildcard>
+                            return get_wildcard_options(pattern)
+                        else:
+                            # $$opt1|opt2|...
+                            options[0] = pattern
+                            return options
+
                     if select_range is not None and len(multi_select_pattern) == 2:
                         # PATTERN: count$$
-                        matches = re.findall(wildcard_pattern, multi_select_pattern[1])
-                        if len(options) == 1 and matches:
-                            # count$$<single wildcard>
-                            options = local_wildcard_dict.get(matches[0])
-                        else:
-                            # count$$opt1|opt2|...
-                            options[0] = multi_select_pattern[1]
+                        options = expand_wildcard_or_return_string(options, multi_select_pattern[1], wildcard_pattern )
                     elif select_range is not None and len(multi_select_pattern) == 3:
                         # PATTERN: count$$ sep $$
                         select_sep = multi_select_pattern[1]
-                        options[0] = multi_select_pattern[2]
+                        options = expand_wildcard_or_return_string(options, multi_select_pattern[2], wildcard_pattern )
 
             adjusted_probabilities = []
 
             total_prob = 0
 
             for option in options:
-                parts = option.split('::', 1)
+                parts = option.split('::', 1) if isinstance(option, str) else f"{option}".split('::', 1)
+
                 if len(parts) == 2 and is_numeric_string(parts[0].strip()):
                     config_value = float(parts[0].strip())
                 else:
@@ -178,15 +189,30 @@ def process(text, seed=None):
             if select_range is None:
                 select_count = 1
             else:
-                select_count = random_gen.integers(low=select_range[0], high=select_range[1]+1, size=1)
+                def calculate_max(_options_length, _max_select_range):
+                    return min(_max_select_range + 1, _options_length + 1) if _max_select_range > 0 else _options_length + 1
 
-            if select_count > len(options):
+                def calculate_select_count(_max_value, _min_select_range, random_gen):
+                    if max(_max_value, _min_select_range) <= 0:
+                        return 0
+                    # fix: low >= high
+                    elif _max_value == _min_select_range:
+                        return _max_value
+                    else:
+                        # fix: low >= high
+                        _low_value = min(_min_select_range, _max_value)
+                        _high_value = max(_min_select_range, _max_value)
+                        return random_gen.integers(low=_low_value, high=_high_value, size=1)
+                select_count = calculate_select_count(calculate_max(len(options), select_range[1]), select_range[0], random_gen)
+
+            if select_count > len(options) or total_prob <= 1:
                 random_gen.shuffle(options)
                 selected_items = options
             else:
                 selected_items = random_gen.choice(options, p=normalized_probabilities, size=select_count, replace=False)
 
-            selected_items2 = [re.sub(r'^\s*[0-9.]+::', '', x, 1) for x in selected_items]
+            # x may be numpy.int32, convert to string
+            selected_items2 = [re.sub(r'^\s*[0-9.]+::', '', str(x), count=1) for x in selected_items]
             replacement = select_sep.join(selected_items2)
             if '::' in replacement:
                 pass
@@ -194,13 +220,41 @@ def process(text, seed=None):
             replacements_found = True
             return replacement
 
-        pattern = r'{([^{}]*?)}'
+        pattern = r'(?<!\\)\{((?:[^{}]|(?<=\\)[{}])*?)(?<!\\)\}'
         replaced_string = re.sub(pattern, replace_option, string)
 
         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
@@ -209,7 +263,23 @@ def process(text, seed=None):
             keyword = match.lower()
             keyword = wildcard_normalize(keyword)
             if keyword in local_wildcard_dict:
-                replacement = random_gen.choice(local_wildcard_dict[keyword])
+                # look for adjusted probability
+                adjusted_probabilities = []
+                total_prob = 0
+                options=local_wildcard_dict[keyword]
+                for option in options:
+                    parts = option.split('::', 1)
+                    if len(parts) == 2 and is_numeric_string(parts[0].strip()):
+                        config_value = float(parts[0].strip())
+                    else:
+                        config_value = 1  # Default value if no configuration is provided
+
+                    adjusted_probabilities.append(config_value)
+                    total_prob += config_value
+
+                normalized_probabilities = [prob / total_prob for prob in adjusted_probabilities]
+                selected_item = random_gen.choice(options, p=normalized_probabilities, replace=False)
+                replacement = re.sub(r'^\s*[0-9.]+::', '', selected_item, count=1)
                 replacements_found = True
                 string = string.replace(f"__{match}__", replacement, 1)
             elif '*' in keyword:
@@ -259,7 +329,7 @@ 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):
@@ -289,6 +359,7 @@ def extract_lora_values(string):
         lbw = None
         lbw_a = None
         lbw_b = None
+        loader = None
 
         if len(item) > 0:
             lora = item[0]
@@ -307,6 +378,8 @@ def extract_lora_values(string):
                             lbw_b = safe_float(lbw_item[2:].strip())
                         elif lbw_item.strip() != '':
                             lbw = lbw_item
+                elif sub_item.startswith("LOADER="):
+                    loader = sub_item[7:]
 
         if a is None:
             a = 1.0
@@ -314,7 +387,7 @@ def extract_lora_values(string):
             b = a
 
         if lora is not None and lora not in added:
-            result.append((lora, a, b, lbw, lbw_a, lbw_b))
+            result.append((lora, a, b, lbw, lbw_a, lbw_b, loader))
             added.add(lora)
 
     return result
@@ -338,6 +411,8 @@ def resolve_lora_name(lora_name_cache, name):
             if x.endswith(name):
                 return x
 
+    return None
+
 
 def process_with_loras(wildcard_opt, model, clip, clip_encoder=None, seed=None, processed=None):
     """
@@ -358,7 +433,7 @@ def process_with_loras(wildcard_opt, model, clip, clip_encoder=None, seed=None,
     loras = extract_lora_values(pass1)
     pass2 = remove_lora_tags(pass1)
 
-    for lora_name, model_weight, clip_weight, lbw, lbw_a, lbw_b in loras:
+    for lora_name, model_weight, clip_weight, lbw, lbw_a, lbw_b, loader in loras:
         lora_name_ext = lora_name.split('.')
         if ('.'+lora_name_ext[-1]) not in folder_paths.supported_pt_extensions:
             lora_name = lora_name+".safetensors"
@@ -372,26 +447,36 @@ def process_with_loras(wildcard_opt, model, clip, clip_encoder=None, seed=None,
             path = None
 
         if path is not None:
-            print(f"LOAD LORA: {lora_name}: {model_weight}, {clip_weight}, LBW={lbw}, A={lbw_a}, B={lbw_b}")
+            logging.info(f"LOAD LORA: {lora_name}: {model_weight}, {clip_weight}, LBW={lbw}, A={lbw_a}, B={lbw_b}, LOADER={loader}")
 
-            def default_lora():
-                return nodes.LoraLoader().load_lora(model, clip, lora_name, model_weight, clip_weight)
-
-            if lbw is not None:
-                if 'LoraLoaderBlockWeight //Inspire' not in nodes.NODE_CLASS_MAPPINGS:
-                    utils.try_install_custom_node(
-                        'https://github.com/ltdrdata/ComfyUI-Inspire-Pack',
-                        "To use 'LBW=' syntax in wildcards, 'Inspire Pack' extension is required.")
-
-                    print(f"'LBW(Lora Block Weight)' is given, but the 'Inspire Pack' is not installed. The LBW= attribute is being ignored.")
-                    model, clip = default_lora()
+            if loader is not None:
+                if loader == 'nunchaku':
+                    if 'NunchakuFluxLoraLoader' not in nodes.NODE_CLASS_MAPPINGS:
+                        logging.warning(f"To use `LOADER=nunchaku`, 'ComfyUI-nunchaku' is required. The LOADER= attribute is being ignored.")
+                    cls = nodes.NODE_CLASS_MAPPINGS['NunchakuFluxLoraLoader']
+                    model = cls().load_lora(model, lora_name, model_weight)[0]
                 else:
-                    cls = nodes.NODE_CLASS_MAPPINGS['LoraLoaderBlockWeight //Inspire']
-                    model, clip, _ = cls().doit(model, clip, lora_name, model_weight, clip_weight, False, 0, lbw_a, lbw_b, "", lbw)
+                    logging.warning(f"LORA LOADER NOT FOUND: '{loader}'")
             else:
-                model, clip = default_lora()
+                def default_lora():
+                    return nodes.LoraLoader().load_lora(model, clip, lora_name, model_weight, clip_weight)
+
+                if lbw is not None:
+                    if 'LoraLoaderBlockWeight //Inspire' not in nodes.NODE_CLASS_MAPPINGS:
+                        utils.try_install_custom_node(
+                            'https://github.com/ltdrdata/ComfyUI-Inspire-Pack',
+                            "To use 'LBW=' syntax in wildcards, 'Inspire Pack' extension is required.")
+
+                        logging.warning(f"'LBW(Lora Block Weight)' is given, but the 'Inspire Pack' is not installed. The LBW= attribute is being ignored.")
+                        model, clip = default_lora()
+                    else:
+                        cls = nodes.NODE_CLASS_MAPPINGS['LoraLoaderBlockWeight //Inspire']
+                        model, clip, _ = cls().doit(model, clip, lora_name, model_weight, clip_weight, False, 0, lbw_a, lbw_b, "", lbw)
+
+                else:
+                    model, clip = default_lora()
         else:
-            print(f"LORA NOT FOUND: {orig_lora_name}")
+            logging.warning(f"LORA NOT FOUND: {orig_lora_name}")
 
     pass3 = [x.strip() for x in pass2.split("BREAK")]
     pass3 = [x for x in pass3 if x != '']
@@ -400,7 +485,7 @@ def process_with_loras(wildcard_opt, model, clip, clip_encoder=None, seed=None,
         pass3 = ['']
 
     pass3_str = [f'[{x}]' for x in pass3]
-    print(f"CLIP: {str.join(' + ', pass3_str)}")
+    logging.info(f"CLIP: {str.join(' + ', pass3_str)}")
 
     result = None
 

pyproject.toml

@@ -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 = "8.18"
 license = { file = "LICENSE.txt" }
 dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]
 

requirements.txt

@@ -3,7 +3,8 @@ scikit-image
 piexif
 transformers
 opencv-python-headless
-GitPython
 scipy>=1.11.4
-numpy<2
-dill
+numpy
+dill
+matplotlib
+git+https://github.com/facebookresearch/sam2