bump version

fixed: install.py - user friendly message with exception handling
modified: requirements.txt remove pinning <2 for numpy
2025-05-19 08:33:30 +09:00 · 2025-05-19 08:31:07 +09:00 · 2025-05-19 05:16:00 +09:00 · 2025-05-18 18:14:42 +09:00 · 2025-05-16 04:42:26 +09:00 · 2025-05-16 03:59:10 +09:00
43 changed files with 11515 additions and 481 deletions
@@ -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 }}
@@ -7,3 +7,5 @@ subpack
 impact_subpack
 *.txt
 *.yaml
+!requirements.txt
+!LICENSE.txt
@@ -32,6 +32,31 @@ NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pac
 * 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.
@@ -68,6 +93,8 @@ NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pac
  * `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.
@@ -105,6 +132,8 @@ NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pac
  * `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.
@@ -222,7 +251,7 @@ NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pac


 ### 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.
@@ -239,6 +268,7 @@ NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pac
  * `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.
@@ -273,6 +303,7 @@ NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pac
      * 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
@@ -288,60 +319,25 @@ NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pac
  * `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.


-## 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.
-
-
 ## Feature
 * `Interactive SAM Detector (Clipspace)` - When you right-click on a node that has 'MASK' and 'IMAGE' outputs, a context menu will open. From this menu, you can either open a dialog to create a SAM Mask using 'Open in SAM Detector', or copy the content (likely mask data) using 'Copy (Clipspace)' and generate a mask using 'Impact SAM Detector' from the clipspace menu, and then paste it using 'Paste (Clipspace)'.
 * 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 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
+## How To Install?

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

 ### 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-manual.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-manual.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.
@@ -363,9 +359,6 @@ mmdet_skip = False
   * (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
@@ -388,17 +381,16 @@ sam_editor_model = sam_vit_b_01ec64.pth
 ```


-## Other Materials (auto-download on initial startup)
+## Other Materials (auto-download when installing)

-* ComfyUI/models/mmdets/bbox <= https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/bbox/mmdet_anime-face_yolov3.pth
-* ComfyUI/models/mmdets/bbox <= https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/mmdet_anime-face_yolov3.py
 * ComfyUI/models/sams <= https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth

+
 ## Troubleshooting page
 * [Troubleshooting Page](troubleshooting/TROUBLESHOOTING.md)


-## How to use (DDetailer feature)
+## How To Use (DDetailer feature)

 #### 1. Basic auto face detection and refine exapmle.
 ![simple](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple.png)
@@ -18,7 +18,6 @@ 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})")

 # Core
@@ -137,6 +136,8 @@ NODE_CLASS_MAPPINGS = {
    "BitwiseAndMask": BitwiseAndMask,
    "SubtractMask": SubtractMask,
    "AddMask": AddMask,
+    "MaskRectArea": MaskRectArea,
+    "MaskRectAreaAdvanced": MaskRectAreaAdvanced,
    "ImpactSegsAndMask": SegsBitwiseAndMask,
    "ImpactSegsAndMaskForEach": SegsBitwiseAndMaskForEach,
    "EmptySegs": EmptySEGS,
@@ -233,6 +234,7 @@ NODE_CLASS_MAPPINGS = {
    "ImpactMakeAnyList": MakeAnyList,
    "ImpactMakeMaskList": MakeMaskList,
    "ImpactMakeMaskBatch": MakeMaskBatch,
+    "ImpactSelectNthItemOfAnyList": NthItemOfAnyList,

    "RegionalSampler": RegionalSampler,
    "RegionalSamplerAdvanced": RegionalSamplerAdvanced,
@@ -246,6 +248,8 @@ NODE_CLASS_MAPPINGS = {
    "ImpactSEGSLabelFilter": SEGSLabelFilter,
    "ImpactSEGSRangeFilter": SEGSRangeFilter,
    "ImpactSEGSOrderedFilter": SEGSOrderedFilter,
+    "ImpactSEGSIntersectionFilter": SEGSIntersectionFilter,
+    "ImpactSEGSNMSFilter": SEGSNMSFilter,

    "ImpactCompare": ImpactCompare,
    "ImpactConditionalBranch": ImpactConditionalBranch,
@@ -318,6 +322,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "BitwiseAndMask": "Pixelwise(MASK & MASK)",
    "SubtractMask": "Pixelwise(MASK - MASK)",
    "AddMask": "Pixelwise(MASK + MASK)",
+    "MaskRectArea": "Mask Rect Area",
+    "MaskRectAreaAdvanced": "Mask Rect Area (Advanced)",
    "ImpactFlattenMask": "Flatten Mask Batch",
    "DetailerForEach": "Detailer (SEGS)",
    "DetailerForEachPipe": "Detailer (SEGS/pipe)",
@@ -359,6 +365,8 @@ 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",
@@ -400,6 +408,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "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",
@@ -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
+}
@@ -68,8 +68,6 @@ def process_wrap(cmd_str, cwd=None, handler=None, env=None):


 try:
-    import platform
-    import folder_paths
    from torchvision.datasets.utils import download_url
    import impact.config

@@ -100,7 +98,7 @@ try:
                if not os.path.exists(os.path.join(sam_path, "sam_vit_b_01ec64.pth")):
                    download_url("https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth", sam_path)
            except:
-                print(f"[Impact Pack] Failed to auto-download model files. Please download them manually.")
+                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})")
@@ -109,13 +107,21 @@ try:
        impact.config.write_config()

        # Remove legacy subpack
-        subpack_path = os.path.join(os.path.dirname(__file__), 'impact_subpack')
-        if os.path.exists(subpack_path):
-            shutil.rmtree(subpack_path)
-            print(f"Legacy subpack is detected. '{subpack_path}' is removed.")
+        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()
@@ -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; }
-					});
-				}
-			}
-		}
-	}
-});
@@ -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")) {
@@ -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;
 						}
@@ -366,20 +428,27 @@ 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");
@@ -442,6 +511,15 @@ 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(!link_info)
 					return;

@@ -453,7 +531,7 @@ 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 {
@@ -484,12 +562,12 @@ app.registerExtension({
 					if(this.inputs[0].type == '*'){
 						const node = app.graph.getNodeById(link_info.origin_id);
 						let origin_type = node.outputs[link_info.origin_slot]?.type;
-						if(link_info.target_slot == 0 && this.inputs.length > 1) {
+						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 == '*') {
+						if(origin_type == '*' && app.graph.getNodeById(link_info.origin_id).slots[link_info.origin_slot].type != '*') {
 							this.disconnectInput(link_info.target_slot);
 							return;
 						}
@@ -507,15 +585,8 @@ 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
@@ -525,6 +596,7 @@ app.registerExtension({
 					}
 				}

+
 				let slot_i = 1;
 				for (let i = 0; i < this.inputs.length; i++) {
 					let input_i = this.inputs[i];
@@ -534,18 +606,13 @@ 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?.length) {
-					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;
 				}
 			}
 		}
@@ -588,12 +655,14 @@ app.registerExtension({

 		if(node.comfyClass == "ImpactSEGSLabelFilter" || node.comfyClass == "SEGSLabelFilterDetailerHookProvider") {
 			node.widgets[0].callback = (value, canvas, node, pos, e) => {
-				if(node.widgets[1].value.trim() != "" && !node.widgets[1].value.trim().endsWith(","))
-					node.widgets[1].value += ", "
+				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;
+					node.widgets[1].value += value;
+					if(node.widgets_values)
+						node.widgets_values[1] = node.widgets[1].value;
+				}
 			}

 			Object.defineProperty(node.widgets[0], "value", {
@@ -667,18 +736,20 @@ app.registerExtension({
 					break;
 			}

-            node.widgets[combo_id+1].callback = (value, canvas, node, pos, e) => {
-                    if(node.widgets[tbox_id].value != '')
-                        node.widgets[tbox_id].value += ', '
+			node.widgets[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;
-            }
+						node.widgets[tbox_id].value += node._wildcard_value;
+					}
+			}

 			Object.defineProperty(node.widgets[combo_id+1], "value", {
 				set: (value) => {
-                    if (value !== "Select the Wildcard to add to the text")
-                        node._wildcard_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"; }
 			});

@@ -691,14 +762,16 @@ app.registerExtension({

 			if(has_lora) {
 				node.widgets[combo_id].callback = (value, canvas, node, pos, e) => {
-					let lora_name = node._value;
-					if(lora_name.endsWith('.safetensors')) {
-						lora_name = lora_name.slice(0, -12);
-					}
+					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:${lora_name}>`;
-					if(node.widgets_values) {
-						node.widgets_values[tbox_id] = node.widgets[tbox_id].value;
+						node.widgets[tbox_id].value += `<lora:${lora_name}>`;
+						if(node.widgets_values) {
+							node.widgets_values[tbox_id] = node.widgets[tbox_id].value;
+						}
 					}
 				}

@@ -730,14 +803,20 @@ 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';
 					 }
 			});
 		}
@@ -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);
@@ -356,13 +356,13 @@ class ImpactSamEditorDialog extends ComfyDialog {
 			let w = (drawWidth * imgCanvas.clientWidth/imgCanvas.width) + "px";
 			let h = (drawHeight * imgCanvas.clientHeight/imgCanvas.height) + "px";

-			pointsCanvas.width = drawWidth;
-			pointsCanvas.height = drawHeight;
+			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.style.width = w;
-			maskCanvas.style.height = h;
+			maskCanvas.width = pointsCanvas.width;
+			maskCanvas.height = pointsCanvas.height;
 			maskCanvas.style.top = imgCanvas.offsetTop + "px";
 			maskCanvas.style.left = imgCanvas.offsetLeft + "px";

@@ -476,8 +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);
 		}
@@ -511,8 +512,8 @@ class ImpactSamEditorDialog extends ComfyDialog {
 			const x = event.offsetX || event.targetTouches[0].clientX - maskRect.left;
 			const y = event.offsetY || event.targetTouches[0].clientY - maskRect.top;

-			const originalX = x * self.image.width / self.pointsCanvas.width;
-			const originalY = y * self.image.height / self.pointsCanvas.height;
+			const originalX = x * self.image.width / self.pointsCanvas.clientWidth;
+			const originalY = y * self.image.height / self.pointsCanvas.clientHeight;

 			var point = null;
 			if (event.button == 0) {
@@ -1,20 +0,0 @@
-import { ComfyApp, app } from "../../scripts/app.js";
-import { api } from "../../scripts/api.js";
-
-let refresh_btn = document.getElementById('comfy-refresh-button');
-let refresh_btn2 = document.querySelector('button[title="Refresh widgets in nodes to find new models or files"]');
-
-let orig = refresh_btn.onclick;
-
-if(refresh_btn) {
-    refresh_btn.onclick = function() {
-        orig();
-        api.fetchApi('/impact/wildcards/refresh');
-    };
-}
-
-if(refresh_btn2) {
-    refresh_btn2?.addEventListener('click', function() {
-        api.fetchApi('/impact/wildcards/refresh');
-    });
-}
@@ -0,0 +1,381 @@
+import { app } from "/scripts/app.js";
+function showPreviewCanvas(node, app) {
+
+    const widget = {
+        type: "customCanvas",
+        name: "mask-rect-area-canvas",
+        get value() {
+            return this.canvas.value;
+        },
+        set value(x) {
+            this.canvas.value = x;
+        },
+        draw: function (ctx, node, widgetWidth, widgetY) {
+
+            // If we are initially offscreen when created we wont have received a resize event
+            // Calculate it here instead
+            if (!node.canvasHeight) {
+                computeCanvasSize(node, node.size);
+            }
+
+            const visible = true;
+            const t = ctx.getTransform();
+            const margin = 12;
+            const border = 2;
+            const widgetHeight = node.canvasHeight;
+            const width = Math.round(node.properties["width"]);
+            const height = Math.round(node.properties["height"]);
+            const scale = Math.min((widgetWidth - margin * 3) / width, (widgetHeight - margin * 3) / height);
+            const blurRadius = node.properties["blur_radius"] || 0;
+            const index = 0;
+
+            Object.assign(this.canvas.style, {
+                left: `${t.e}px`,
+                top: `${t.f + (widgetY * t.d)}px`,
+                width: `${widgetWidth * t.a}px`,
+                height: `${widgetHeight * t.d}px`,
+                position: "absolute",
+                zIndex: 1,
+                fontSize: `${t.d * 10.0}px`,
+                pointerEvents: "none"
+            });
+
+            this.canvas.hidden = !visible;
+
+            let backgroundWidth = width * scale;
+            let backgroundHeight = height * scale;
+
+            let xOffset = margin;
+            if (backgroundWidth < widgetWidth) {
+                xOffset += (widgetWidth - backgroundWidth) / 2 - margin;
+            }
+            let yOffset = (margin / 2);
+            if (backgroundHeight < widgetHeight) {
+                yOffset += (widgetHeight - backgroundHeight) / 2 - margin;
+            }
+
+            let widgetX = xOffset;
+            widgetY = widgetY + yOffset;
+
+            // Draw the background border
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR;
+            ctx.fillRect(widgetX - border, widgetY - border, backgroundWidth + border * 2, backgroundHeight + border * 2)
+
+            // Draw the main background area 
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR;
+            ctx.fillRect(widgetX, widgetY, backgroundWidth, backgroundHeight);
+
+            // Draw the conditioning zone
+            let [x, y, w, h] = getDrawArea(node, backgroundWidth, backgroundHeight);
+
+            ctx.fillStyle = getDrawColor(0, "80");
+            ctx.fillRect(widgetX + x, widgetY + y, w, h);
+            ctx.beginPath();
+            ctx.lineWidth = 1;
+
+            // Draw grid lines
+            for (let x = 0; x <= width / 64; x += 1) {
+                ctx.moveTo(widgetX + x * 64 * scale, widgetY);
+                ctx.lineTo(widgetX + x * 64 * scale, widgetY + backgroundHeight);
+            }
+
+            for (let y = 0; y <= height / 64; y += 1) {
+                ctx.moveTo(widgetX, widgetY + y * 64 * scale);
+                ctx.lineTo(widgetX + backgroundWidth, widgetY + y * 64 * scale);
+            }
+
+            ctx.strokeStyle = "#66666650";
+            ctx.stroke();
+            ctx.closePath();
+
+            // Draw current zone
+            let [sx, sy, sw, sh] = getDrawArea(node, backgroundWidth, backgroundHeight);
+
+            ctx.fillStyle = getDrawColor(0, "80");
+            ctx.fillRect(widgetX + sx, widgetY + sy, sw, sh);
+
+            ctx.fillStyle = getDrawColor(0, "40");
+            ctx.fillRect(widgetX + sx + border, widgetY + sy + border, sw - border * 2, sh - border * 2);
+
+            // Draw white border around the current zone
+            ctx.strokeStyle = globalThis.LiteGraph.NODE_SELECTED_TITLE_COLOR;
+            ctx.lineWidth = 2;
+            ctx.strokeRect(widgetX + sx, widgetY + sy, sw, sh);
+
+            // Display
+            ctx.beginPath();
+
+            ctx.arc(LiteGraph.NODE_SLOT_HEIGHT * 0.5, LiteGraph.NODE_SLOT_HEIGHT * (index + 0.5) + 4, 4, 0, Math.PI * 2);
+            ctx.fill();
+
+            ctx.lineWidth = 1;
+            ctx.strokeStyle = "white";
+            ctx.stroke();
+
+            ctx.lineWidth = 1;
+            ctx.closePath();
+
+            // Draw progress bar canvas
+            if (backgroundWidth < widgetWidth) {
+                xOffset += (widgetWidth - backgroundWidth) / 2 - margin;
+            }
+
+            // Ajustar las coordenadas X e Y
+            const barHeight = 8;
+            let widgetYBar = widgetY + backgroundHeight + margin;
+
+            // Dibujar el borde negro alrededor de la barra
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR;
+            ctx.fillRect(
+                    widgetX - border,
+                    widgetYBar - border,
+                    backgroundWidth + border * 2,
+                    barHeight + border * 2
+                    );
+
+            // Dibujar el área principal de la barra (fondo)
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR; // Mismo color de fondo que el canvas
+            ctx.fillRect(
+                    widgetX,
+                    widgetYBar,
+                    backgroundWidth,
+                    barHeight
+                    );
+
+
+            // Draw progress bar grid
+            ctx.beginPath();
+            ctx.lineWidth = 1;
+            ctx.strokeStyle = "#66666650";
+
+            // Calcular el número de líneas en función del tamaño de la barra
+            const numLines = Math.floor(backgroundWidth / 64);
+
+            // Dibujar líneas del grid
+            for (let x = 0; x <= width / 64; x += 1) {
+                ctx.moveTo(widgetX + x * 64 * scale, widgetYBar);
+                ctx.lineTo(widgetX + x * 64 * scale, widgetYBar + barHeight);
+            }
+            ctx.stroke();
+            ctx.closePath();
+
+            // Dibujar progreso (basado en blur_radius)
+            const progress = Math.min(blurRadius / 255, 1);
+            ctx.fillStyle = "rgba(0, 120, 255, 0.5)";
+
+            ctx.fillRect(
+                    widgetX,
+                    widgetYBar,
+                    backgroundWidth * progress,
+                    barHeight
+                    );
+        }
+    };
+
+    widget.canvas = document.createElement("canvas");
+    widget.canvas.className = "mask-rect-area-canvas";
+    widget.parent = node;
+
+    document.body.appendChild(widget.canvas);
+    node.addCustomWidget(widget);
+
+    app.canvas.onDrawBackground = function () {
+        // Draw node isnt fired once the node is off the screen
+        // if it goes off screen quickly, the input may not be removed
+        // this shifts it off screen so it can be moved back if the node is visible.
+        for (let n in app.graph._nodes) {
+            n = app.graph._nodes[n];
+            for (let w in n.widgets) {
+                let wid = n.widgets[w];
+                if (Object.hasOwn(wid, "canvas")) {
+                    wid.canvas.style.left = -8000 + "px";
+                    wid.canvas.style.position = "absolute";
+                }
+            }
+        }
+    };
+
+    node.onResize = function (size) {
+        computeCanvasSize(node, size);
+    };
+
+    return {minWidth: 200, minHeight: 200, widget};
+}
+
+app.registerExtension({
+    name: 'drltdata.MaskRectAreaAdvanced',
+    async beforeRegisterNodeDef(nodeType, nodeData, app) {
+        if (nodeData.name === "MaskRectAreaAdvanced") {
+            const onNodeCreated = nodeType.prototype.onNodeCreated;
+            nodeType.prototype.onNodeCreated = function () {
+                const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined;
+
+                this.setProperty("width", 512);
+                this.setProperty("height", 512);
+                this.setProperty("x", 0);
+                this.setProperty("y", 0);
+                this.setProperty("w", 256);
+                this.setProperty("h", 256);
+                this.setProperty("blur_radius", 0);
+
+                this.selected = false;
+                this.index = 3;
+                this.serialize_widgets = true;
+
+                CUSTOM_INT(this, "x", 0, function (v, _, node) {
+                    const s = this.options.step / 10;
+                    this.value = Math.round(v / s) * s;
+                    node.properties["x"] = this.value;
+                });
+                CUSTOM_INT(this, "y", 0, function (v, _, node) {
+                    const s = this.options.step / 10;
+                    this.value = Math.round(v / s) * s;
+                    node.properties["y"] = this.value;
+                });
+                CUSTOM_INT(this, "width", 256, function (v, _, node) {
+                    const s = this.options.step / 10;
+                    this.value = Math.round(v / s) * s;
+                    node.properties["w"] = this.value;
+                });
+                CUSTOM_INT(this, "height", 256, function (v, _, node) {
+                    const s = this.options.step / 10;
+                    this.value = Math.round(v / s) * s;
+                    node.properties["h"] = this.value;
+                });
+                CUSTOM_INT(this, "image_width", 512, function (v, _, node) {
+                    const s = this.options.step / 10;
+                    this.value = Math.round(v / s) * s;
+                    node.properties["width"] = this.value;
+                });
+                CUSTOM_INT(this, "image_height", 512, function (v, _, node) {
+                    const s = this.options.step / 10;
+                    this.value = Math.round(v / s) * s;
+                    node.properties["height"] = this.value;
+                });
+                CUSTOM_INT(this, "blur_radius", 0, function (v, _, node) {
+                    this.value = Math.round(v) || 0;
+                    node.properties["blur_radius"] = this.value;
+                },
+                        {"min": 0, "max": 255, "step": 10}
+                );
+
+                showPreviewCanvas(this, app);
+
+                this.onSelected = function () {
+                    this.selected = true;
+                };
+                this.onDeselected = function () {
+                    this.selected = false;
+                };
+
+                return r;
+            };
+        }
+    }
+});
+
+// Calculate the drawing area using individual properties.
+function getDrawArea(node, backgroundWidth, backgroundHeight) {
+    let x = node.properties["x"] * backgroundWidth / node.properties["width"];
+    let y = node.properties["y"] * backgroundHeight / node.properties["height"];
+    let w = node.properties["w"] * backgroundWidth / node.properties["width"];
+    let h = node.properties["h"] * backgroundHeight / node.properties["height"];
+
+    if (x > backgroundWidth) {
+        x = backgroundWidth;
+    }
+    if (y > backgroundHeight) {
+        y = backgroundHeight;
+    }
+
+    if (x + w > backgroundWidth) {
+        w = Math.max(0, backgroundWidth - x);
+    }
+
+    if (y + h > backgroundHeight) {
+        h = Math.max(0, backgroundHeight - y);
+    }
+
+    return [x, y, w, h];
+}
+
+function CUSTOM_INT(node, inputName, val, func, config = {}) {
+    return {
+        widget: node.addWidget(
+                "number",
+                inputName,
+                val,
+                func,
+                Object.assign({}, {min: 0, max: 4096, step: 640, precision: 0}, config)
+                )
+    };
+}
+
+function getDrawColor(percent, alpha) {
+    let h = 360 * percent;
+    let s = 50;
+    let l = 50;
+    l /= 100;
+    const a = s * Math.min(l, 1 - l) / 100;
+    const f = n => {
+        const k = (n + h / 30) % 12;
+        const color = l - a * Math.max(Math.min(k - 3, 9 - k, 1), -1);
+        return Math.round(255 * color).toString(16).padStart(2, '0');   // convert to Hex and prefix "0" if needed
+    };
+    return `#${f(0)}${f(8)}${f(4)}${alpha}`;
+}
+
+function computeCanvasSize(node, size) {
+    if (node.widgets[0].last_y == null) {
+        return;
+    }
+
+    const MIN_HEIGHT = 220;
+    const MIN_WIDTH = 240;
+
+    let y = LiteGraph.NODE_WIDGET_HEIGHT * Math.max(node.inputs.length, node.outputs.length) + 5;
+    let freeSpace = size[1] - y;
+
+    // Compute the height of all non-customCanvas widgets
+    let widgetHeight = 0;
+    for (let i = 0; i < node.widgets.length; i++) {
+        const w = node.widgets[i];
+        if (w.type !== "customCanvas") {
+            if (w.computeSize) {
+                widgetHeight += w.computeSize()[1] + 4;
+            } else {
+                widgetHeight += LiteGraph.NODE_WIDGET_HEIGHT + 5;
+            }
+        }
+    }
+
+    // Ensure there is enough vertical space
+    freeSpace -= widgetHeight;
+
+    // Adjust the height of the node if needed
+    if (freeSpace < MIN_HEIGHT) {
+        freeSpace = MIN_HEIGHT;
+        node.size[1] = y + widgetHeight + freeSpace;
+        node.graph.setDirtyCanvas(true);
+    }
+
+    // Ensure the node width meets the minimum width requirement
+    if (node.size[0] < MIN_WIDTH) {
+        node.size[0] = MIN_WIDTH;
+        node.graph.setDirtyCanvas(true);
+    }
+
+    // Position each of the widgets
+    for (const w of node.widgets) {
+        w.y = y;
+        if (w.type === "customCanvas") {
+            y += freeSpace;
+        } else if (w.computeSize) {
+            y += w.computeSize()[1] + 4;
+        } else {
+            y += LiteGraph.NODE_WIDGET_HEIGHT + 4;
+        }
+    }
+
+    node.canvasHeight = freeSpace;
+}
@@ -0,0 +1,366 @@
+import { app } from "/scripts/app.js";
+function showPreviewCanvas(node, app) {
+
+    const widget = {
+        type: "customCanvas",
+        name: "mask-rect-area-canvas",
+        get value() {
+            return this.canvas.value;
+        },
+        set value(x) {
+            this.canvas.value = x;
+        },
+        draw: function (ctx, node, widgetWidth, widgetY) {
+
+            // If we are initially offscreen when created we wont have received a resize event
+            // Calculate it here instead
+            if (!node.canvasHeight) {
+                computeCanvasSize(node, node.size);
+            }
+
+            const visible = true;
+            const t = ctx.getTransform();
+            const margin = 12;
+            const border = 2;
+            const widgetHeight = node.canvasHeight;
+            const width = 512;
+            const height = 512;
+            const scale = Math.min((widgetWidth - margin * 3) / width, (widgetHeight - margin * 3) / height);
+            const blurRadius = node.properties["blur_radius"] || 0;
+            const index = 0;
+
+            Object.assign(this.canvas.style, {
+                left: `${t.e}px`,
+                top: `${t.f + (widgetY * t.d)}px`,
+                width: `${widgetWidth * t.a}px`,
+                height: `${widgetHeight * t.d}px`,
+                position: "absolute",
+                zIndex: 1,
+                fontSize: `${t.d * 10.0}px`,
+                pointerEvents: "none"
+            });
+
+            this.canvas.hidden = !visible;
+
+            let backgroundWidth = width * scale;
+            let backgroundHeight = height * scale;
+            let xOffset = margin;
+            if (backgroundWidth < widgetWidth) {
+                xOffset += (widgetWidth - backgroundWidth) / 2 - margin;
+            }
+            let yOffset = (margin / 2);
+            if (backgroundHeight < widgetHeight) {
+                yOffset += (widgetHeight - backgroundHeight) / 2 - margin;
+            }
+
+            let widgetX = xOffset;
+            widgetY = widgetY + yOffset;
+
+            // Draw the background border
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR;
+            ctx.fillRect(widgetX - border, widgetY - border, backgroundWidth + border * 2, backgroundHeight + border * 2);
+
+            // Draw the main background area 
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR;
+            ctx.fillRect(widgetX, widgetY, backgroundWidth, backgroundHeight);
+
+            // Draw the conditioning zone
+            let [x, y, w, h] = getDrawArea(node, backgroundWidth, backgroundHeight);
+
+            ctx.fillStyle = getDrawColor(0, "80");
+            ctx.fillRect(widgetX + x, widgetY + y, w, h);
+            ctx.beginPath();
+            ctx.lineWidth = 1;
+
+            // Draw grid lines
+            for (let x = 0; x <= width / 64; x += 1) {
+                ctx.moveTo(widgetX + x * 64 * scale, widgetY);
+                ctx.lineTo(widgetX + x * 64 * scale, widgetY + backgroundHeight);
+            }
+
+            for (let y = 0; y <= height / 64; y += 1) {
+                ctx.moveTo(widgetX, widgetY + y * 64 * scale);
+                ctx.lineTo(widgetX + backgroundWidth, widgetY + y * 64 * scale);
+            }
+
+            ctx.strokeStyle = "#66666650";
+            ctx.stroke();
+            ctx.closePath();
+
+            // Draw current zone
+            let [sx, sy, sw, sh] = getDrawArea(node, backgroundWidth, backgroundHeight);
+
+            ctx.fillStyle = getDrawColor(0, "80");
+            ctx.fillRect(widgetX + sx, widgetY + sy, sw, sh);
+
+            ctx.fillStyle = getDrawColor(0, "40");
+            ctx.fillRect(widgetX + sx + border, widgetY + sy + border, sw - border * 2, sh - border * 2);
+
+            // Draw white border around the current zone
+            ctx.strokeStyle = globalThis.LiteGraph.NODE_SELECTED_TITLE_COLOR;
+            ctx.lineWidth = 2;
+            ctx.strokeRect(widgetX + sx, widgetY + sy, sw, sh);
+            //ctx.strokeRect(finalSX, finalSY, finalSW, finalSH);
+
+            // Display
+            ctx.beginPath();
+
+            ctx.arc(LiteGraph.NODE_SLOT_HEIGHT * 0.5, LiteGraph.NODE_SLOT_HEIGHT * (index + 0.5) + 4, 4, 0, Math.PI * 2);
+            ctx.fill();
+
+            ctx.lineWidth = 1;
+            ctx.strokeStyle = "white";
+            ctx.stroke();
+            ctx.lineWidth = 1;
+            ctx.closePath();
+
+            // Draw progress bar canvas
+            if (backgroundWidth < widgetWidth) {
+                xOffset += (widgetWidth - backgroundWidth) / 2 - margin;
+            }
+
+            const barHeight = 8;
+            let widgetYBar = widgetY + backgroundHeight + margin;
+
+            // Draw progress bar border
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR;
+            ctx.fillRect(
+                    widgetX - border,
+                    widgetYBar - border,
+                    backgroundWidth + border * 2,
+                    barHeight + border * 2
+                    );
+
+            // Draw progress bar area
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR; // Mismo color de fondo que el canvas
+            ctx.fillRect(
+                    widgetX,
+                    widgetYBar,
+                    backgroundWidth,
+                    barHeight
+                    );
+
+            // Draw progress bar grid
+            ctx.beginPath();
+            ctx.lineWidth = 1;
+            ctx.strokeStyle = "#66666650";
+
+            // Determine max lines
+            const numLines = Math.floor(backgroundWidth / 64);
+
+            // Draw progress bar grid
+            for (let x = 0; x <= width / 64; x += 1) {
+                ctx.moveTo(widgetX + x * 64 * scale, widgetYBar);
+                ctx.lineTo(widgetX + x * 64 * scale, widgetYBar + barHeight);
+            }
+            ctx.stroke();
+            ctx.closePath();
+
+            // Draw progress bar
+            const progress = Math.min(blurRadius / 255, 1);
+            ctx.fillStyle = "rgba(0, 120, 255, 0.5)";
+
+            ctx.fillRect(
+                    widgetX,
+                    widgetYBar,
+                    backgroundWidth * progress,
+                    barHeight
+                    );
+        }
+    };
+
+    widget.canvas = document.createElement("canvas");
+    widget.canvas.className = "mask-rect-area-canvas";
+    widget.parent = node;
+
+    document.body.appendChild(widget.canvas);
+    node.addCustomWidget(widget);
+
+    app.canvas.onDrawBackground = function () {
+        // Draw node isnt fired once the node is off the screen
+        // if it goes off screen quickly, the input may not be removed
+        // this shifts it off screen so it can be moved back if the node is visible.
+        for (let n in app.graph._nodes) {
+            n = app.graph._nodes[n];
+            for (let w in n.widgets) {
+                let wid = n.widgets[w];
+                if (Object.hasOwn(wid, "canvas")) {
+                    wid.canvas.style.left = -8000 + "px";
+                    wid.canvas.style.position = "absolute";
+                }
+            }
+        }
+    };
+
+    node.onResize = function (size) {
+        computeCanvasSize(node, size);
+    };
+
+    return {minWidth: 200, minHeight: 200, widget};
+}
+
+app.registerExtension({
+    name: 'drltdata.MaskRectArea',
+    async beforeRegisterNodeDef(nodeType, nodeData, app) {
+        if (nodeData.name === "MaskRectArea") {
+            const onNodeCreated = nodeType.prototype.onNodeCreated;
+            nodeType.prototype.onNodeCreated = function () {
+                const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined;
+
+                this.setProperty("width", 512);
+                this.setProperty("height", 512);
+                this.setProperty("x", 0);
+                this.setProperty("y", 0);
+                this.setProperty("w", 50);
+                this.setProperty("h", 50);
+                this.setProperty("blur_radius", 0);
+
+                this.selected = false;
+                this.index = 3;
+                this.serialize_widgets = true;
+
+                CUSTOM_INT(this, "x", 0, function (v, _, node) {
+                    this.value = Math.max(0, Math.min(100, Math.round(v))); // Limitar entre 0 y 100
+                    node.properties["x"] = this.value;
+                });
+                CUSTOM_INT(this, "y", 0, function (v, _, node) {
+                    this.value = Math.max(0, Math.min(100, Math.round(v)));
+                    node.properties["y"] = this.value;
+                });
+                CUSTOM_INT(this, "w", 50, function (v, _, node) {
+                    this.value = Math.max(0, Math.min(100, Math.round(v)));
+                    node.properties["w"] = this.value;
+                });
+                CUSTOM_INT(this, "h", 50, function (v, _, node) {
+                    this.value = Math.max(0, Math.min(100, Math.round(v)));
+                    node.properties["h"] = this.value;
+                });
+                CUSTOM_INT(this, "blur_radius", 0, function (v, _, node) {
+                    this.value = Math.round(v) || 0;
+                    node.properties["blur_radius"] = this.value;
+                },
+                        {"min": 0, "max": 255, "step": 10}
+                );
+
+                showPreviewCanvas(this, app);
+
+                this.onSelected = function () {
+                    this.selected = true;
+                };
+                this.onDeselected = function () {
+                    this.selected = false;
+                };
+
+                return r;
+            };
+        }
+    }
+});
+
+// Calculate the drawing area using percentage-based properties.
+function getDrawArea(node, backgroundWidth, backgroundHeight) {
+    // Convert percentages to actual pixel values based on the background dimensions
+    let x = (node.properties["x"] / 100) * backgroundWidth;
+    let y = (node.properties["y"] / 100) * backgroundHeight;
+    let w = (node.properties["w"] / 100) * backgroundWidth;
+    let h = (node.properties["h"] / 100) * backgroundHeight;
+
+    // Ensure the values do not exceed the background boundaries
+    if (x > backgroundWidth) {
+        x = backgroundWidth;
+    }
+    if (y > backgroundHeight) {
+        y = backgroundHeight;
+    }
+
+    // Adjust width and height to fit within the background dimensions
+    if (x + w > backgroundWidth) {
+        w = Math.max(0, backgroundWidth - x);
+    }
+    if (y + h > backgroundHeight) {
+        h = Math.max(0, backgroundHeight - y);
+    }
+
+    return [x, y, w, h];
+}
+
+function CUSTOM_INT(node, inputName, val, func, config = {}) {
+    return {
+        widget: node.addWidget(
+                "number",
+                inputName,
+                val,
+                func,
+                Object.assign({}, {min: 0, max: 100, step: 10, precision: 0}, config)
+                )
+    };
+}
+
+function getDrawColor(percent, alpha) {
+    let h = 360 * percent;
+    let s = 50;
+    let l = 50;
+    l /= 100;
+    const a = s * Math.min(l, 1 - l) / 100;
+    const f = n => {
+        const k = (n + h / 30) % 12;
+        const color = l - a * Math.max(Math.min(k - 3, 9 - k, 1), -1);
+        return Math.round(255 * color).toString(16).padStart(2, '0');   // convert to Hex and prefix "0" if needed
+    };
+    return `#${f(0)}${f(8)}${f(4)}${alpha}`;
+}
+
+function computeCanvasSize(node, size) {
+    if (node.widgets[0].last_y == null) {
+        return;
+    }
+
+    const MIN_HEIGHT = 200;
+    const MIN_WIDTH = 200;
+
+    let y = LiteGraph.NODE_WIDGET_HEIGHT * Math.max(node.inputs.length, node.outputs.length) + 5;
+    let freeSpace = size[1] - y;
+
+    // Compute the height of all non-customCanvas widgets
+    let widgetHeight = 0;
+    for (let i = 0; i < node.widgets.length; i++) {
+        const w = node.widgets[i];
+        if (w.type !== "customCanvas") {
+            if (w.computeSize) {
+                widgetHeight += w.computeSize()[1] + 4;
+            } else {
+                widgetHeight += LiteGraph.NODE_WIDGET_HEIGHT + 5;
+            }
+        }
+    }
+
+    // Ensure there is enough vertical space
+    freeSpace -= widgetHeight;
+
+    // Adjust the height of the node if needed
+    if (freeSpace < MIN_HEIGHT) {
+        freeSpace = MIN_HEIGHT;
+        node.size[1] = y + widgetHeight + freeSpace;
+        node.graph.setDirtyCanvas(true);
+    }
+
+    // Ensure the node width meets the minimum width requirement
+    if (node.size[0] < MIN_WIDTH) {
+        node.size[0] = MIN_WIDTH;
+        node.graph.setDirtyCanvas(true);
+    }
+
+    // Position each of the widgets
+    for (const w of node.widgets) {
+        w.y = y;
+        if (w.type === "customCanvas") {
+            y += freeSpace;
+        } else if (w.computeSize) {
+            y += w.computeSize()[1] + 4;
+        } else {
+            y += LiteGraph.NODE_WIDGET_HEIGHT + 4;
+        }
+    }
+
+    node.canvasHeight = freeSpace;
+}
@@ -27,7 +27,7 @@ class SEGSDetailerForAnimateDiff:
                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
                     "scheduler": (core.SCHEDULERS,),
                     "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
-                     "basic_pipe": ("BASIC_PIPE",),
+                     "basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
                     "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
                     },
                "optional": {
@@ -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,
@@ -1,7 +1,7 @@
 import configparser
 import os

-version_code = [8, 0]
+version_code = [8, 15, 1]
 version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')

 dependency_version = 24
@@ -48,7 +48,7 @@ preview_bridge_last_mask_cache = {}

 current_prompt = None

-SCHEDULERS = comfy.samplers.KSampler.SCHEDULERS + ['AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]', 'LTXV[default]']
+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():
@@ -244,7 +244,8 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
                   detailer_hook=None,
                   refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None,
                   refiner_negative=None, control_net_wrapper=None, cycle=1,
-                   inpaint_model=False, noise_mask_feather=0, scheduler_func=None):
+                   inpaint_model=False, noise_mask_feather=0, scheduler_func=None,
+                   vae_tiled_encode=False, vae_tiled_decode=False):

    if noise_mask is not None:
        noise_mask = utils.tensor_gaussian_blur_mask(noise_mask, noise_mask_feather)
@@ -334,7 +335,7 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
            print(f"[Impact Pack] ComfyUI is an outdated version.")
            positive, negative, latent_image = imc_encode(positive, negative, upscaled_image, vae, noise_mask)
    else:
-        latent_image = to_latent_image(upscaled_image, vae)
+        latent_image = to_latent_image(upscaled_image, vae, vae_tiled_encode=vae_tiled_encode)
        if noise_mask is not None:
            latent_image['noise_mask'] = noise_mask

@@ -369,12 +370,18 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
        refined_latent = detailer_hook.pre_decode(refined_latent)

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

    if detailer_hook is not None:
        refined_image = detailer_hook.post_decode(refined_image)
@@ -1384,9 +1391,14 @@ def vae_decode(vae, samples, use_tile, hook, tile_size=512, overlap=64):
    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]

@@ -1412,7 +1424,7 @@ 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, overlap=64):
@@ -1433,7 +1445,7 @@ 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, overlap=64):
@@ -1464,7 +1476,7 @@ 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,
@@ -1500,7 +1512,7 @@ def latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_mod
    if hook is not None:
        pixels = hook.post_upscale(pixels)

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


 class TwoSamplersForMaskUpscaler:
@@ -1670,8 +1682,14 @@ class PixelKSampleUpscaler:
                preprocessor = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']()
                # might add capacity to set pyrUp_iters later, not needed for now though
                preprocessed = preprocessor.execute(images, pyrUp_iters=3, resolution=min(image_w, image_h))[0]
-                apply_cnet = getattr(nodes.ControlNetApply(), nodes.ControlNetApply.FUNCTION)
-                positive = apply_cnet(positive, self.tile_cnet, preprocessed, strength=self.tile_cnet_strength)[0]
+                positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive=positive,
+                                                                                      negative=negative,
+                                                                                      control_net=self.tile_cnet,
+                                                                                      image=preprocessed,
+                                                                                      strength=self.tile_cnet_strength,
+                                                                                      start_percent=0,
+                                                                                      end_percent=1.0,
+                                                                                      vae=self.vae)

        refined_latent = impact_sampling.impact_sample(model, seed, steps, cfg, sampler_name, scheduler,
                                                       positive, negative, upscaled_latent, denoise, scheduler_func=self.scheduler_func)
@@ -1942,7 +1960,7 @@ class PixelTiledKSampleUpscaler:
    def __init__(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative,
                 denoise,
                 tile_width, tile_height, tiling_strategy,
-                 upscale_model_opt=None, hook_opt=None, tile_cnet_opt=None, tile_size=512, tile_cnet_strength=1.0):
+                 upscale_model_opt=None, hook_opt=None, tile_cnet_opt=None, tile_size=512, tile_cnet_strength=1.0, overlap=64):
        self.params = scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
        self.vae = vae
        self.tile_params = tile_width, tile_height, tiling_strategy
@@ -1952,6 +1970,7 @@ class PixelTiledKSampleUpscaler:
        self.tile_size = tile_size
        self.is_tiled = True
        self.tile_cnet_strength = tile_cnet_strength
+        self.overlap = overlap

    def tiled_ksample(self, latent, images):
        if "BNK_TiledKSampler" in nodes.NODE_CLASS_MAPPINGS:
@@ -1974,8 +1993,14 @@ class PixelTiledKSampleUpscaler:
                preprocessor = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']()
                # might add capacity to set pyrUp_iters later, not needed for now though
                preprocessed = preprocessor.execute(images, pyrUp_iters=3, resolution=min(image_w, image_h))[0]
-                apply_cnet = getattr(nodes.ControlNetApply(), nodes.ControlNetApply.FUNCTION)
-                positive = apply_cnet(positive, self.tile_cnet, preprocessed, strength=self.tile_cnet_strength)[0]
+
+                positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive=positive,
+                                                                                      negative=negative,
+                                                                                      control_net=self.tile_cnet,
+                                                                                      image=preprocessed,
+                                                                                      strength=self.tile_cnet_strength,
+                                                                                      start_percent=0, end_percent=1.0,
+                                                                                      vae=self.vae)

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


 try:
@@ -95,9 +96,13 @@ class SAMLoader:
    @classmethod
    def INPUT_TYPES(cls):
        models = [x for x in folder_paths.get_filename_list("sams") if 'hq' not in x]
+
+        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."}),
@@ -191,7 +196,7 @@ 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 +222,8 @@ 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"}),
                   }
                }

@@ -225,6 +232,8 @@ 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
@@ -233,7 +242,7 @@ class DetailerForEach:
    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.')
@@ -269,7 +278,7 @@ 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):
@@ -297,13 +306,16 @@ 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 = [
@@ -324,16 +336,21 @@ class DetailerForEach:
                break

            orig_cropped_image = cropped_image.clone()
-            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)
+            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)
@@ -376,13 +393,15 @@ 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, )

@@ -405,7 +424,7 @@ 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}),

@@ -417,6 +436,8 @@ 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"}),
                     }
                }

@@ -427,10 +448,13 @@ 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.')
@@ -448,7 +472,8 @@ 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:
@@ -462,7 +487,7 @@ 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}),
@@ -505,6 +530,8 @@ 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")
@@ -514,6 +541,8 @@ 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,
@@ -522,7 +551,7 @@ 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')
@@ -554,7 +583,8 @@ 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 = []
@@ -580,7 +610,8 @@ 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
@@ -598,7 +629,8 @@ 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
@@ -986,6 +1018,7 @@ 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}),
                    }
                }

@@ -995,11 +1028,11 @@ 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',
@@ -1312,7 +1345,11 @@ 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]

@@ -1334,7 +1371,7 @@ 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}),
@@ -1368,6 +1405,8 @@ 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"}),
                   }
                }

@@ -1378,11 +1417,14 @@ 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
@@ -1405,7 +1447,8 @@ 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
@@ -1471,6 +1514,8 @@ 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,
@@ -1539,7 +1584,7 @@ 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.')
@@ -1548,7 +1593,8 @@ 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:
@@ -1575,9 +1621,12 @@ 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.')
@@ -1596,7 +1645,8 @@ 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:
@@ -1834,6 +1884,135 @@ def get_file_item(base_type, path):
           }


+class MaskRectArea:
+    # Creates a rectangle mask using percentage.
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+            },
+            "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"}
+        }
+
+    RETURN_TYPES = ("MASK",)
+    
+    CATEGORY = "ImpactPack/Operation"
+    FUNCTION = "create_mask"
+
+    def create_mask(self, extra_pnginfo, unique_id, **kwargs):
+        # search for node
+        node_found = False
+        for node in extra_pnginfo["workflow"]["nodes"]:
+            if node["id"] == int(unique_id):
+                min_x = node["properties"].get("x", 0) / 100
+                min_y = node["properties"].get("y", 0) / 100
+                width = node["properties"].get("w", 0) / 100
+                height = node["properties"].get("h", 0) / 100
+                blur_radius = node["properties"].get("blur_radius", 0)
+                node_found = True
+                break
+                
+        if not node_found:
+            raise ValueError(f"No node found with unique_id {unique_id}.")
+                
+        # Create a mask with standard resolution (e.g., 512x512)
+        resolution = 512
+        mask = torch.zeros((resolution, resolution))
+
+        # Calculate pixel coordinates
+        min_x_px = int(min_x * resolution)
+        min_y_px = int(min_y * resolution)
+        max_x_px = int((min_x + width) * resolution)
+        max_y_px = int((min_y + height) * resolution)
+
+        # Draw the rectangle on the mask
+        mask[min_y_px:max_y_px, min_x_px:max_x_px] = 1
+
+        # Apply blur if the radii are greater than 0
+        if blur_radius > 0:
+            dx = blur_radius * 2 + 1
+            dy = blur_radius * 2 + 1
+
+            # Convert the mask to a format compatible with OpenCV (numpy array)
+            mask_np = mask.cpu().numpy().astype("float32")
+
+            # Apply Gaussian Blur
+            blurred_mask = cv2.GaussianBlur(mask_np, (dx, dy), 0)
+
+            # Convert back to tensor
+            mask = torch.from_numpy(blurred_mask)
+
+        # Return the mask as a tensor with an additional channel
+        return (mask.unsqueeze(0),)
+
+
+class MaskRectAreaAdvanced:
+    # Creates a rectangle mask using pixels relative to image size.
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+            },
+            "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"}
+        }
+
+    RETURN_TYPES = ("MASK",)
+    
+    CATEGORY = "ImpactPack/Operation"
+    FUNCTION = "create_mask_advanced"
+
+    def create_mask_advanced(self, extra_pnginfo, unique_id, **kwargs):
+        # search for node
+        node_found = False
+        for node in extra_pnginfo["workflow"]["nodes"]:
+            if node["id"] == int(unique_id):
+                min_x = node["properties"]["x"]
+                min_y = node["properties"]["y"]
+                width = node["properties"]["w"]
+                height = node["properties"]["h"]
+                image_width = node["properties"]["width"]
+                image_height = node["properties"]["height"]
+                blur_radius = node["properties"]["blur_radius"]
+                node_found = True
+                break
+                
+        if not node_found:
+            raise ValueError(f"No node found with unique_id {unique_id}.")
+
+        # Calculate maximum coordinates
+        max_x = min_x + width
+        max_y = min_y + height
+
+        # Create a mask with the image dimensions
+        mask = torch.zeros((image_height, image_width))
+
+        # Draw the rectangle on the mask
+        mask[int(min_y):int(max_y), int(min_x):int(max_x)] = 1
+
+        # Apply blur if the radii are greater than 0
+        if blur_radius > 0:
+            dx = blur_radius * 2 + 1
+            dy = blur_radius * 2 + 1
+
+            # Convert the mask to a format compatible with OpenCV (numpy array)
+            mask_np = mask.cpu().numpy().astype("float32")
+
+            # Apply Gaussian Blur
+            blurred_mask = cv2.GaussianBlur(mask_np, (dx, dy), 0)
+
+            # Convert back to tensor
+            mask = torch.from_numpy(blurred_mask)
+
+        # Return the mask as a tensor with an additional channel
+        return (mask.unsqueeze(0),)
+
+
 class ImageReceiver:
    @classmethod
    def INPUT_TYPES(s):
@@ -2180,7 +2359,11 @@ class ImpactWildcardProcessor:
        return {"required": {
                        "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "Enter a prompt using wildcard syntax."}),
                        "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "The actual value passed during the execution of 'ImpactWildcardProcessor' is what is shown here. The behavior varies slightly depending on the mode. Wildcard syntax can also be used in 'populated_text'."}),
-                        "mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed", "tooltip": "Populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\nFixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode."}),
+                        "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"],),
                    },
@@ -2189,9 +2372,10 @@ class ImpactWildcardProcessor:
    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'.")
+                   "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
@@ -2211,8 +2395,10 @@ class ImpactWildcardEncode:
                        "clip": ("CLIP",),
                        "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "Enter a prompt using wildcard syntax."}),
                        "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "The actual value passed during the execution of 'ImpactWildcardEncode' is what is shown here. The behavior varies slightly depending on the mode. Wildcard syntax can also be used in 'populated_text'."}),
-                        "mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed", "tooltip": "Populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n"
-                                                                                                                       "Fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode."}),
+                        "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, "tooltip": "Determines the random seed to be used for wildcard processing."}),
@@ -2222,7 +2408,7 @@ class ImpactWildcardEncode:
    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"
+                   "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")
@@ -2249,7 +2435,7 @@ 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]', 'LTXV[default]'],),
+            "extra_scheduler": (['None', 'AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]', 'LTXV[default]', 'OSS FLUX', 'OSS Wan'],),
        }}

    CATEGORY = "ImpactPack/Util"
@@ -29,6 +29,8 @@ def calculate_sigmas(model, sampler, scheduler, steps):
        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)

@@ -46,65 +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)
@@ -17,11 +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
+import logging


 sam_predictor = None
@@ -77,9 +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"])

@@ -92,10 +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)


@@ -107,7 +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")
@@ -178,7 +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})


@@ -324,20 +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']:
@@ -346,7 +354,7 @@ 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

@@ -377,6 +385,8 @@ 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)
@@ -396,6 +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()

@@ -466,7 +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]]
@@ -479,25 +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):
@@ -542,7 +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

@@ -8,7 +8,7 @@ from impact.utils import any_typ
 import impact.core as core
 import re
 import nodes
-import traceback
+

 class ImpactCompare:
    @classmethod
@@ -574,27 +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 = {}
@@ -701,6 +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'])

@@ -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
@@ -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):
@@ -1482,6 +1580,8 @@ class SEGSPicker:

    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:
@@ -1538,6 +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 = []
@@ -446,6 +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):
@@ -526,6 +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"
@@ -533,14 +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)

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


 class TensorBatchBuilder:
@@ -177,31 +178,71 @@ 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 +542,21 @@ 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):
@@ -8,6 +8,7 @@ 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"))
@@ -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
@@ -63,7 +66,7 @@ def read_wildcard_dict(wildcard_path):
                    with open(file_path, 'r', encoding="UTF-8", errors="ignore") as f:
                        lines = f.read().splitlines()
                        wildcard_dict[key] = [x for x in lines if not x.strip().startswith('#')]
-            elif file.endswith('.yaml'):
+            elif file.endswith('.yaml') or file.endswith('.yml'):
                file_path = os.path.join(root, file)

                try:
@@ -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$$<single wildcard>
-                            options = get_wildcard_options(multi_select_pattern[1])
-                        else:
-                            # count$$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), 1) for x in selected_items]
            replacement = select_sep.join(selected_items2)
            if '::' in replacement:
                pass
@@ -237,7 +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, 1)
                replacements_found = True
                string = string.replace(f"__{match}__", replacement, 1)
            elif '*' in keyword:
@@ -317,6 +359,7 @@ def extract_lora_values(string):
        lbw = None
        lbw_a = None
        lbw_b = None
+        loader = None

        if len(item) > 0:
            lora = item[0]
@@ -335,6 +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
@@ -342,7 +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
@@ -366,6 +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):
    """
@@ -386,7 +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"
@@ -400,26 +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 != '']
@@ -428,7 +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

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-impact-pack"
 description = "This node pack offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler."
-version = "8.0"
+version = "8.15.1"
 license = { file = "LICENSE.txt" }
 dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]

@@ -4,6 +4,6 @@ piexif
 transformers
 opencv-python-headless
 scipy>=1.11.4
-numpy<2
+numpy
 dill
 matplotlib
Author	SHA1	Message	Date
Dr.Lt.Data	f8e16df2be	bump version	2025-05-19 08:33:30 +09:00
Dr.Lt.Data	7df60b2107	fixed: install.py - user friendly message with exception handling	2025-05-19 08:31:07 +09:00
Dr.Lt.Data	b394c158ea	modified: requirements.txt remove pinning <2 for numpy	2025-05-19 05:16:00 +09:00
Dr.Lt.Data	3e3cf3a5b4	support nunchaku lora loading in the wildcard feature https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/993	2025-05-18 18:14:42 +09:00
Dr.Lt.Data	93fc248503	fixed: cannot connect wildcard input/output to the Switch / InversedSwitch nodes https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/992	2025-05-16 04:42:26 +09:00
Dr.Lt.Data	16ffa7d462	hotfix: potential front error ``` TypeError: Cannot read properties of undefined (reading 'type') ``` https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/989	2025-05-16 03:59:10 +09:00
filtered	cd34cfdd63	Fix empty widgets array throws in console (#988 )	2025-05-03 14:37:31 +09:00
Dr.Lt.Data	38bb9ffdf6	fixed: Switch - buggy behavior on firefox browser https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/983#issuecomment-2833471721	2025-04-28 00:54:31 +09:00
Dr.Lt.Data	f939e66e1c	fixed: this.widgets is undefined https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/977	2025-04-27 22:16:11 +09:00
robo	b22aa90cf4	allow for .yml files in wildcards (#966 ) both are valid yaml extensions so it makes sense to allow both	2025-04-25 18:50:40 +09:00
Dr.Lt.Data	d18aaecb93	bump version	2025-04-25 18:46:18 +09:00
だにえる	a7840b4fbf	feat: tensor_paste supports RGBA images with alpha channel blending (#962 ) This enhancement adds support for properly handling RGBA images in the tensor_paste function. The previous implementation caused errors when pasting images with alpha channels. Now the function can handle all combinations of RGB and RGBA images: - RGB to RGB (unchanged) - RGBA to RGBA (with proper alpha compositing) - RGB to RGBA - RGBA to RGB Fix for the error: "RuntimeError: The size of tensor a (4) must match the size of tensor b (3) at non-singleton dimension 3"	2025-04-25 18:35:03 +09:00
robo	efa0cb66b2	always use impact.wildcards.process fixes server side wildcards.process not getting new wildcard_dict (#967 )	2025-04-25 18:21:55 +09:00
Dr.Lt.Data	0b94005b1b	hotfix: impactswitch - frontend crash https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/970#issuecomment-2827153556	2025-04-25 08:03:20 +09:00
Dr.Lt.Data	bfcb8674b3	feat: Select Nth Item (Any list) https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/971	2025-04-24 18:00:37 +09:00
Dr.Lt.Data	839e5a9f90	fixed: ImpactSwitch - slot was not shrinked when disconnecting fixed: ImpactSwitch - invalid upper bounding of the select value https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/972	2025-04-24 12:38:35 +09:00
Dr.Lt.Data	5a2adda580	fixed: impact switch - compatibility patch with new front https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/964	2025-04-22 02:06:08 +09:00
Dr.Lt.Data	d05882a9e0	refactor: remove useless legacy code	2025-04-22 01:33:58 +09:00
Dr.Lt.Data	d900939861	feat: Impact Sampler - support OSS scheduler	2025-04-13 10:24:35 +09:00
Jaquan	cd2696f6fd	fix #946 InversedSwitch(Any) was not append outputs after first connect (#956 ) InversedSwitch(Any) not increasing number of outputs after connecting first output (in ) # 953 #946 In the latest ComfyUI_frontend version, output is not appended as expected	2025-04-12 19:01:00 +09:00
Dr.Lt.Data	0b1ac0f1c5	bump version to v8.10	2025-03-24 00:06:42 +09:00
chutchatut	216d7fd60c	Added sort=None to SEGS order filter (#944 ) * added pick first N SEGs node * remove take first N segs node and update segs order filter to add order=None * add comments * update for readibility * format code * remove match statement	2025-03-24 00:06:06 +09:00
chutchatut	28cd2f70b2	Update README.md (#943 )	2025-03-21 19:06:24 +09:00
Dr.Lt.Data	2708eba825	bump version v8.9	2025-03-20 21:36:25 +09:00
chutchatut	c19ab92172	add intersection and non max suppression SEGS filter (#940 )	2025-03-20 21:21:15 +09:00
Robin Huangandsnomiao	6e3d07277b	chore(publish): update workflow for node publishing with conditional execution and permissions (#935 ) Co-authored-by: snomiao <snomiao+comfy-pr@gmail.com>	2025-03-17 00:19:33 +09:00
Dr.Lt.Data	782c6f439e	fixed: controlbridge - better error message - mute/bypass behavior cannot be used in api mode https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/933	2025-03-12 18:10:42 +09:00
Dr.Lt.Data	0e3e6a193a	improved: SAM Loader - don't add 'ESAM' if 'ComfyUI-YoloWorld-EfficientSAM' is not installed	2025-03-04 22:44:31 +09:00
Dr.Lt.Data	798776838e	remove sample_error_enhancer	2025-03-04 12:43:03 +09:00
Dr.Lt.Data	66493d8cb4	formatting..	2025-03-02 16:56:59 +09:00
Dr.Lt.Data	b4fd0834e0	fixed: crash when loading api json https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/919	2025-03-02 16:54:16 +09:00
Dr.Lt.Data	808b0dedf0	update README.md	2025-02-23 10:03:04 +09:00
Dr.Lt.Data	c6056b132d	add example workflow	2025-02-15 11:04:03 +09:00
Dr.Lt.Data	1ae7cae2df	version marker	2025-02-02 15:05:41 +09:00
izmp	ccb6285548	Fixed several issues related to number handling and wildcard processing (#896 )	2025-02-02 15:04:57 +09:00
Dr.Lt.Data	092310bc8f	refactor: impact_sampling	2025-01-31 21:01:03 +09:00
Dr.Lt.Data	5c530eb32e	fixed: wildcards - cannot edit populated_text on 'fixed' mode https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/898	2025-01-30 16:55:01 +09:00
Dr.Lt.Data	e35bc23fd1	add locales partly. added: more descriptions added: locales/ko	2025-01-30 16:49:23 +09:00
Dr.Lt.Data	869ac6fd1f	version marker	2025-01-28 12:21:26 +09:00
Dijkstra	cb168d64ab	feat: allow wildcard file to use the adjusted probabilities feature (#891 )	2025-01-28 12:16:10 +09:00
Dr.Lt.Data	a89e9e01a6	fixed: sam editor - cannot load sam model properly when sams directory is specified manually. https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/895	2025-01-28 06:35:51 +09:00
Dr.Lt.Data	e70b4df9b5	fixed: `reproduce` mode is reflected to the saved metadata but it wans't	2025-01-27 11:05:12 +09:00
Dr.Lt.Data	af1ef7e441	feat: wildcards nodes - `reproduce` mode is added. https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/882 fixed: js crash https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/886#issuecomment-2613922083 removed: combo bool migration script	2025-01-27 10:55:47 +09:00
Dr.Lt.Data	d8738eee2f	hotfix: front - robustness fix https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/886#issuecomment-2600716281	2025-01-19 18:08:59 +09:00
Dr.Lt.Data	c397c68ca3	version marker	2025-01-19 03:17:25 +09:00
Alex Butler	0e0722ec08	Add tiled vae encoding/decoding toggle to detailer nodes (#883 ) * detailers: support optional tiled vae encoding & decoding * Remove start encoding/decoding logging	2025-01-19 03:16:56 +09:00
Dr.Lt.Data	70d0540895	improved: tooltips	2025-01-14 00:47:27 +09:00
Dr.Lt.Data	7330577a0f	version marker	2025-01-10 00:49:38 +09:00
Symbiomatrix	b1d760291f	Dummy shortcircuit. (#880 )	2025-01-10 00:49:06 +09:00
Dr.Lt.Data	12e838a320	improved: refresh wildcard Now refresh feature is moved to menu item. `Edit > Impact: Refresh Wildcard`	2025-01-06 22:59:33 +09:00
Dr.Lt.Data	8e8621df49	update .gitignore	2024-12-30 02:32:46 +09:00
Dr.Lt.Data	cdb7b4d3b0	fix: compatibility patch for VAEEncodeTiled's overlap https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/863#issuecomment-2560861500	2024-12-25 16:14:51 +09:00
Dr.Lt.Data	21eecb0c03	fix: install script - folder_paths error	2024-12-22 18:40:09 +09:00
Dr.Lt.Data	9402ecf4f9	refactor: use ControlNetApplyAdvanced instead of legacy ControlNetApply	2024-12-21 18:21:58 +09:00
Dr.Lt.Data	c21b361e2a	fix: VAEEncodeTiled - overlap compatibility patch https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/863	2024-12-21 17:41:24 +09:00
Dr.Lt.Data	8f04714145	fixed: inversed_switch - If the `select` is pointing to an out-of-range output, remove the input connection instead of the output. https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/851#issuecomment-2543353847	2024-12-19 23:29:34 +09:00
Dr.Lt.Data	a311f9278d	fixed: sam detector - invalid position https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/853	2024-12-19 23:06:49 +09:00
Dr.Lt.Data	0ec21f1a4b	version marker	2024-12-19 22:03:11 +09:00
Andrés Zsögön	40a3df3acd	Added Mask Rect Area and Mask Rect Area (Advanced) nodes (#861 ) Added nodes for simple rectangular masks of arbitrary size and position with built-in preview canvas. Closes issue #856.	2024-12-19 22:02:40 +09:00
Dr.Lt.Data	9ba92862c4	update README.md	2024-12-17 11:03:02 +09:00
Dr.Lt.Data	2c2e148205	update README.md	2024-12-17 10:58:17 +09:00
Dr.Lt.Data	ff9c30787c	Robustly remove legacy subpack directories.	2024-12-11 23:24:00 +09:00