bump version

fix: typo in locales
change wildcard {} regex to allow escaping (#968 )
2025-06-19 12:40:42 +09:00 · 2025-06-19 12:39:30 +09:00 · 2025-06-19 12:39:06 +09:00 · 2025-06-17 23:40:50 +09:00 · 2025-06-17 00:46:43 +09:00 · 2025-06-16 12:34:56 +09:00
41 changed files with 10533 additions and 349 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 }}
@@ -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.
@@ -107,6 +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.
@@ -162,6 +189,10 @@ NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pac
  * `PreviewDetailerHook` - Connecting this hook node helps provide assistance for viewing previews whenever SEGS Detailing tasks are completed. When working with a large number of SEGS, such as Make Tile SEGS, it allows for monitoring the situation as improvements progress incrementally.
    * Since this is the hook applied when pasting onto the original image, it has no effect on nodes like `SEGSDetailer`.
  * `VariationNoiseDetailerHookProvider` - Apply variation seed to the detailer. It can be applied in multiple stages through combine.
+  * `CustomSamplerDetailerHookProvider` - Apply a hook that allows you to use a custom sampler in the Detailer nodes. When using `DetailerHookCombine`, the sampler from the first hook is applied.
+  * `LamaRemoverDetailerHookProvider` – Applies Lama Remover to the upscaled image during the detailing stage. If `skip_sampling` is set to True, Lama Remover can be used alone without the detailing stage, allowing it to simply remove detected regions.
+      * Not applicable for **AnimateDiff** detailers. When using `DetailerHookCombine`, `skip_sampling` is only applied if it is set to `True` for all hooks.
+      * To use this node, the node pack at [Layer-norm/comfyui-lama-remover](https://github.com/Layer-norm/comfyui-lama-remover) must be installed.

 ### Iterative Upscale nodes
  * `Iterative Upscale (Latent/on Pixel Space)` - The upscaler takes the input upscaler and splits the scale_factor into steps, then iteratively performs upscaling. 
@@ -224,7 +255,7 @@ 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.
@@ -241,6 +272,7 @@ 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.
@@ -460,3 +492,5 @@ BlenderNeok/[ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) - The
 WASasquatch/[was-node-suite-comfyui](https://github.com/WASasquatch/was-node-suite-comfyui) - A powerful custom node extensions of ComfyUI.

 Trung0246/[ComfyUI-0246](https://github.com/Trung0246/ComfyUI-0246) - Nice bypass hack!
+
+Layer-norm/[comfyui-lama-remover](https://github.com/Layer-norm/comfyui-lama-remover) - Required for using `LamaRemoverDetailerHook`.
@@ -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
@@ -123,6 +122,8 @@ NODE_CLASS_MAPPINGS = {
    "UnsamplerHookProvider": UnsamplerHookProvider,
    "CoreMLDetailerHookProvider": CoreMLDetailerHookProvider,
    "PreviewDetailerHookProvider": PreviewDetailerHookProvider,
+    "CustomSamplerDetailerHookProvider": CustomSamplerDetailerHookProvider,
+    "LamaRemoverDetailerHookProvider": LamaRemoverDetailerHookProvider,

    "DetailerHookCombine": DetailerHookCombine,
    "NoiseInjectionDetailerHookProvider": NoiseInjectionDetailerHookProvider,
@@ -235,6 +236,7 @@ NODE_CLASS_MAPPINGS = {
    "ImpactMakeAnyList": MakeAnyList,
    "ImpactMakeMaskList": MakeMaskList,
    "ImpactMakeMaskBatch": MakeMaskBatch,
+    "ImpactSelectNthItemOfAnyList": NthItemOfAnyList,

    "RegionalSampler": RegionalSampler,
    "RegionalSamplerAdvanced": RegionalSamplerAdvanced,
@@ -248,6 +250,8 @@ NODE_CLASS_MAPPINGS = {
    "ImpactSEGSLabelFilter": SEGSLabelFilter,
    "ImpactSEGSRangeFilter": SEGSRangeFilter,
    "ImpactSEGSOrderedFilter": SEGSOrderedFilter,
+    "ImpactSEGSIntersectionFilter": SEGSIntersectionFilter,
+    "ImpactSEGSNMSFilter": SEGSNMSFilter,

    "ImpactCompare": ImpactCompare,
    "ImpactConditionalBranch": ImpactConditionalBranch,
@@ -363,6 +367,8 @@ 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",
@@ -404,6 +410,7 @@ 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
+}
@@ -68,7 +68,6 @@ def process_wrap(cmd_str, cwd=None, handler=None, env=None):


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

@@ -99,7 +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})")
@@ -108,18 +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.")
-            
-        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.")
+        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()
@@ -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() {
@@ -223,29 +230,29 @@ 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
-			});
-        }
-      }
-    ],
+	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']
-      }
-    ],
+	menuCommands: [
+		{
+			path: ['Edit'],
+			commands: ['refresh-impact-wildcard']
+		}
+	],

 	loadedGraphNode(node, app) {
 		if (node.comfyClass == "MaskPainter") {
@@ -324,6 +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];
@@ -337,12 +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 {
@@ -359,7 +392,7 @@ 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
@@ -371,7 +404,7 @@ app.registerExtension({
 							return; // fallback
 						}

-						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;
 						}
@@ -395,20 +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");
@@ -471,6 +511,23 @@ app.registerExtension({

 			const onConnectionsChange = nodeType.prototype.onConnectionsChange;
 			nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
+				const stackTrace = new Error().stack;
+				if(stackTrace.includes('loadGraphData')) {
+					if(this.widgets?.[0]) {
+						this.widgets[0].options.max = this.inputs.length-3;
+						this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max);
+					}
+					return;
+				}
+
+				if(stackTrace.includes('pasteFromClipboard')) {
+					if(this.widgets?.[0]) {
+						this.widgets[0].options.max = this.inputs.length-3;
+						this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max);
+					}
+					return;
+				}
+
 				if(!link_info)
 					return;

@@ -482,7 +539,7 @@ app.registerExtension({
 						}

 						if(this.outputs[0].type == '*'){
-							if(link_info.type == '*') {
+							if(link_info.type == '*' && app.graph.getNodeById(link_info.target_id).slots[link_info.target_slot].type != '*') {
 								app.graph._nodes_by_id[link_info.target_id].disconnectInput(link_info.target_slot);
 							}
 							else {
@@ -513,12 +570,12 @@ 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;
 						}
@@ -536,15 +593,8 @@ app.registerExtension({
 				}

 				let select_slot = this.inputs.find(x => x.name == "select");
-				let mode_slot = this.inputs.find(x => x.name == "sel_mode");
-
-				let converted_count = 0;
-				converted_count += select_slot?1:0;
-				converted_count += mode_slot?1:0;
-
-				if (!connected && (this.inputs.length > 1+converted_count)) {
-					const stackTrace = new Error().stack;

+				if (!connected && (this.inputs.length > 3)) {
 					if(
 						!stackTrace.includes('LGraphNode.prototype.connect') && // for touch device
 						!stackTrace.includes('LGraphNode.connect') && // for mouse device
@@ -554,6 +604,7 @@ app.registerExtension({
 					}
 				}

+
 				let slot_i = 1;
 				for (let i = 0; i < this.inputs.length; i++) {
 					let input_i = this.inputs[i];
@@ -563,18 +614,13 @@ app.registerExtension({
 					}
 				}

-				let last_slot = this.inputs[this.inputs.length - 1];
-				if (
-					(last_slot.name == 'select' && last_slot.name != 'sel_mode' && this.inputs[this.inputs.length - 2].link != undefined)
-					|| (last_slot.name != 'select' && last_slot.name != 'sel_mode' && last_slot.link != undefined)) {
-						this.addInput(`${input_name}${slot_i}`, this.outputs[0].type);
+				if(connected) {
+					this.addInput(`${input_name}${slot_i}`, this.outputs[0].type);
 				}

-				if(this.widgets?.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;
 				}
 			}
 		}
@@ -617,12 +663,14 @@ 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", {
@@ -696,18 +744,20 @@ 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"; }
 			});

@@ -720,14 +770,16 @@ 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;
+						}
 					}
 				}

@@ -766,7 +818,7 @@ app.registerExtension({
 						else
 							node._mode_value = value; // combo value

-						populated_text_widget.inputEl.disabled = node._mode_value != 'populate';
+						populated_text_widget.inputEl.disabled = node._mode_value == 'populate';
 					},
 				get: () => {
 						if(node._mode_value != undefined)
@@ -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):
@@ -166,6 +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, 6]
+version_code = [8, 17, 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():
@@ -320,6 +320,9 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
    # upscale
    upscaled_image = tensor_resize(image, new_w, new_h)

+    if detailer_hook is not None:
+        upscaled_image = detailer_hook.post_upscale(upscaled_image, noise_mask)
+
    cnet_pils = None
    if control_net_wrapper is not None:
        positive, negative, cnet_pils = control_net_wrapper.apply(positive, negative, upscaled_image, noise_mask)
@@ -327,61 +330,69 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
        cnet_pils.extend(cnet_pils2)

    # prepare mask
-    if noise_mask is not None and inpaint_model:
-        imc_encode = nodes.InpaintModelConditioning().encode
-        if 'noise_mask' in inspect.signature(imc_encode).parameters:
-            positive, negative, latent_image = imc_encode(positive, negative, upscaled_image, vae, mask=noise_mask, noise_mask=True)
+    if detailer_hook is None or not detailer_hook.get_skip_sampling():
+        if noise_mask is not None and inpaint_model:
+            imc_encode = nodes.InpaintModelConditioning().encode
+            if 'noise_mask' in inspect.signature(imc_encode).parameters:
+                positive, negative, latent_image = imc_encode(positive, negative, upscaled_image, vae, mask=noise_mask, noise_mask=True)
+            else:
+                print(f"[Impact Pack] ComfyUI is an outdated version.")
+                positive, negative, latent_image = imc_encode(positive, negative, upscaled_image, vae, noise_mask)
        else:
-            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, vae_tiled_encode=vae_tiled_encode)
-        if noise_mask is not None:
-            latent_image['noise_mask'] = noise_mask
+            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

-    if detailer_hook is not None:
-        latent_image = detailer_hook.post_encode(latent_image)
-
-    refined_latent = latent_image
-
-    # ksampler
-    for i in range(0, cycle):
        if detailer_hook is not None:
+            latent_image = detailer_hook.post_encode(latent_image)
+
+        refined_latent = latent_image
+
+        sampler_opt=None
+        if detailer_hook is not None:
+            sampler_opt = detailer_hook.get_custom_sampler()
+
+        # ksampler
+        for i in range(0, cycle):
            if detailer_hook is not None:
-                detailer_hook.set_steps((i, cycle))
+                if detailer_hook is not None:
+                    detailer_hook.set_steps((i, cycle))

-            refined_latent = detailer_hook.cycle_latent(refined_latent)
+                refined_latent = detailer_hook.cycle_latent(refined_latent)

-            model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \
-                detailer_hook.pre_ksample(model, seed+i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)
-            noise, is_touched = detailer_hook.get_custom_noise(seed+i, torch.zeros(latent_image['samples'].size()), is_touched=False)
-            if not is_touched:
+                model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \
+                    detailer_hook.pre_ksample(model, seed+i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)
+                noise, is_touched = detailer_hook.get_custom_noise(seed+i, torch.zeros(latent_image['samples'].size()), is_touched=False)
+                if not is_touched:
+                    noise = None
+            else:
+                model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \
+                    model, seed + i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise
                noise = None
+
+            refined_latent = impact_sampling.ksampler_wrapper(model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2,
+                                                              refined_latent, denoise2, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative,
+                                                              noise=noise, scheduler_func=scheduler_func, sampler_opt=sampler_opt)
+
+        if detailer_hook is not None:
+            refined_latent = detailer_hook.pre_decode(refined_latent)
+
+        # non-latent downscale - latent downscale cause bad quality
+        start = time.time()
+        if vae_tiled_decode:
+            (refined_image,) = nodes.VAEDecodeTiled().decode(vae, refined_latent, 512) # using default settings
+            print(f"[Impact Pack] vae decoded (tiled) in {time.time() - start:.1f}s")
        else:
-            model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \
-                model, seed + i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise
-            noise = None
-
-        refined_latent = impact_sampling.ksampler_wrapper(model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2,
-                                                          refined_latent, denoise2, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative,
-                                                          noise=noise, scheduler_func=scheduler_func)
-
-    if detailer_hook is not None:
-        refined_latent = detailer_hook.pre_decode(refined_latent)
-
-    # non-latent downscale - latent downscale cause bad quality
-    start = time.time()
-    if vae_tiled_decode:
-        (refined_image,) = nodes.VAEDecodeTiled().decode(vae, refined_latent, 512) # using default settings
-        print(f"[Impact Pack] vae decoded (tiled) in {time.time() - start:.1f}s")
+            try:
+                refined_image = vae.decode(refined_latent['samples'])
+            except Exception as e:
+                # usually an out-of-memory exception from the decode, so try a tiled approach
+                print(f"[Impact Pack] failed after {time.time() - start:.1f}s, doing vae.decode_tiled 64...")
+                refined_image = vae.decode_tiled(refined_latent["samples"], tile_x=64, tile_y=64, )
+            print(f"[Impact Pack] vae decoded in {time.time() - start:.1f}s")
    else:
-        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")
+        # skipped
+        refined_image = upscaled_image

    if detailer_hook is not None:
        refined_image = detailer_hook.post_decode(refined_image)
@@ -513,11 +524,16 @@ def enhance_detail_for_animatediff(image_frames, model, clip, vae, guide_size, g
        'samples': latent_frames
    }

+
+    sampler_opt=None
+    if detailer_hook is not None:
+        sampler_opt = detailer_hook.get_custom_sampler()
+
    if detailer_hook is not None:
        latent = detailer_hook.post_encode(latent)

    refined_latent = impact_sampling.ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
-                                                      latent, denoise, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative, scheduler_func=scheduler_func)
+                                                      latent, denoise, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative, scheduler_func=scheduler_func, sampler_opt=sampler_opt)

    if detailer_hook is not None:
        refined_latent = detailer_hook.pre_decode(refined_latent)
@@ -1077,7 +1093,7 @@ class ONNXDetector:
    def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
        drop_size = max(drop_size, 1)
        try:
-            import impact.onnx as onnx
+            import impact.impact_onnx as onnx

            h = image.shape[1]
            w = image.shape[2]
@@ -163,7 +163,7 @@ class SegmDetectorCombined:
        mask = segm_detector.detect_combined(image, threshold, dilation)

        if mask is None:
-            mask = torch.zeros((image.shape[2], image.shape[1]), dtype=torch.float32, device="cpu")
+            mask = torch.zeros((image.shape[1], image.shape[2]), dtype=torch.float32, device="cpu")

        return (mask.unsqueeze(0),)

@@ -183,7 +183,7 @@ class BboxDetectorCombined(SegmDetectorCombined):
        mask = bbox_detector.detect_combined(image, threshold, dilation)

        if mask is None:
-            mask = torch.zeros((image.shape[2], image.shape[1]), dtype=torch.float32, device="cpu")
+            mask = torch.zeros((image.shape[1], image.shape[2]), dtype=torch.float32, device="cpu")

        return (mask.unsqueeze(0),)

@@ -1,6 +1,8 @@
 import sys
 from . import hooks
 from . import defs
+from . import utils
+import nodes


 class SEGSOrderedFilterDetailerHookProvider:
@@ -83,3 +85,24 @@ class PreviewDetailerHookProvider:
    def doit(self, quality, unique_id):
        hook = hooks.PreviewDetailerHook(unique_id, quality)
        return hook, hook
+
+
+class LamaRemoverDetailerHookProvider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask_threshold":("INT", {"default": 250, "min": 0, "max": 255, "step": 1, "display": "slider"}),
+                "gaussblur_radius": ("INT", {"default": 8, "min": 0, "max": 20, "step": 1, "display": "slider"}),
+                "skip_sampling": ("BOOLEAN", {"default": True}),
+            }
+        }
+
+    RETURN_TYPES = ("DETAILER_HOOK", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, mask_threshold, gaussblur_radius, skip_sampling):
+        hook = hooks.LamaRemoverDetailerHook(mask_threshold, gaussblur_radius, skip_sampling)
+        return (hook, )
@@ -25,7 +25,7 @@ class PixelKSampleHook:
    def post_decode(self, pixels):
        return pixels

-    def post_upscale(self, pixels):
+    def post_upscale(self, pixels, mask=None):
        return pixels

    def post_encode(self, samples):
@@ -64,8 +64,8 @@ class PixelKSampleHookCombine(PixelKSampleHook):
    def post_decode(self, pixels):
        return self.hook2.post_decode(self.hook1.post_decode(pixels))

-    def post_upscale(self, pixels):
-        return self.hook2.post_upscale(self.hook1.post_upscale(pixels))
+    def post_upscale(self, pixels, mask=None):
+        return self.hook2.post_upscale(self.hook1.post_upscale(pixels, mask), mask)

    def post_encode(self, samples):
        return self.hook2.post_encode(self.hook1.post_encode(samples))
@@ -109,6 +109,15 @@ class DetailerHookCombine(PixelKSampleHookCombine):
        noise_2nd, is_touched = self.hook2.get_custom_noise(seed, noise, is_touched)
        return noise, is_touched

+    def get_custom_sampler(self):
+        if self.hook1.get_custom_sampler() is not None:
+            return self.hook1.get_custom_sampler()
+        else:
+            return self.hook2.get_custom_sampler()
+
+    def get_skip_sampling(self):
+        return self.hook1.get_skip_sampling() and self.hook2.get_skip_sampling()
+

 class SimpleCfgScheduleHook(PixelKSampleHook):
    target_cfg = 0
@@ -173,6 +182,21 @@ class DetailerHook(PixelKSampleHook):
    def get_custom_noise(self, seed, noise, is_touched):
        return noise, is_touched

+    def get_custom_sampler(self):
+        return None
+
+    def get_skip_sampling(self):
+        return False
+
+
+class CustomSamplerDetailerHookProvider(DetailerHook):
+    def __init__(self, sampler):
+        super().__init__()
+        self.sampler = sampler
+
+    def get_custom_sampler(self):
+        return self.sampler
+

 # class CustomNoiseDetailerHookProvider(DetailerHook):
 #     def __init__(self, noise):
@@ -486,6 +510,27 @@ class SEGSLabelFilterDetailerHook(DetailerHook):
        return segs_nodes.SEGSLabelFilter().doit(segs, "", self.labels)[0]


+class LamaRemoverDetailerHook(DetailerHook):
+    def __init__(self, mask_threshold, gaussblur_radius, skip_sampling):
+        super().__init__()
+        self.mask_threshold = mask_threshold
+        self.gaussblur_radius = gaussblur_radius
+        self.skip_sampling = skip_sampling
+
+    def post_upscale(self, img, mask=None):
+        if "LamaRemover" in nodes.NODE_CLASS_MAPPINGS:
+            lama_remover_obj = nodes.NODE_CLASS_MAPPINGS['LamaRemover']()
+        else:
+            utils.try_install_custom_node('https://github.com/Layer-norm/comfyui-lama-remover',
+                                          "To use 'LAMARemoverDetailerHookProvider', 'comfyui-lama-remover' nodepack is required.")
+            raise Exception("'LamaRemover' node is not installed.")
+
+        return lama_remover_obj.lama_remover(img, masks=mask, mask_threshold=self.mask_threshold, gaussblur_radius=self.gaussblur_radius, invert_mask=False)[0]
+
+    def get_skip_sampling(self):
+        return self.skip_sampling
+
+
 class PreviewDetailerHook(DetailerHook):
    def __init__(self, node_id, quality):
        super().__init__()
@@ -96,9 +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."}),
@@ -228,6 +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
@@ -442,6 +448,8 @@ 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,
@@ -533,6 +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,
@@ -801,6 +811,26 @@ class CoreMLDetailerHookProvider:
        return (hook, )


+class CustomSamplerDetailerHookProvider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "sampler": ("SAMPLER", ),
+                    },
+                }
+
+    RETURN_TYPES = ("DETAILER_HOOK",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Detailer"
+    
+    DESCRIPTION = "Apply a hook that allows you to use a custom sampler in the Detailer nodes. When using `DetailerHookCombine`, the sampler from the first hook is applied."
+
+    def doit(self, sampler):
+        hook = hooks.CustomSamplerDetailerHookProvider(sampler)
+        return (hook, )
+
+
 class CfgScheduleHookProvider:
    schedules = ["simple"]

@@ -1407,6 +1437,8 @@ 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,
@@ -1502,6 +1534,8 @@ 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,
@@ -1607,6 +1641,8 @@ 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,
@@ -1890,7 +1926,7 @@ class MaskRectArea:
        # search for node
        node_found = False
        for node in extra_pnginfo["workflow"]["nodes"]:
-            if node["id"] == int(unique_id):
+            if str(node["id"]) == unique_id:
                min_x = node["properties"].get("x", 0) / 100
                min_y = node["properties"].get("y", 0) / 100
                width = node["properties"].get("w", 0) / 100
@@ -2343,7 +2379,7 @@ 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": (["populate", "fixed", "reproduce"], {"default": "populate", "tooltip": 
+                        "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."
@@ -2359,6 +2395,7 @@ class ImpactWildcardProcessor:
                   "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
@@ -2418,7 +2455,7 @@ 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)
@@ -230,7 +194,7 @@ def impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, ne


 def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise,
-                     refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, sigma_factor=1.0, noise=None, scheduler_func=None):
+                     refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, sigma_factor=1.0, noise=None, scheduler_func=None, sampler_opt=None):

    if refiner_ratio is None or refiner_model is None or refiner_clip is None or refiner_positive is None or refiner_negative is None:
        # Use separated_sample instead of KSampler for `AYS scheduler`
@@ -242,7 +206,7 @@ def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive,

        refined_latent = separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler,
                                          positive, negative, latent_image, start_at_step, end_at_step, False,
-                                          sigma_ratio=sigma_factor, noise=noise, scheduler_func=scheduler_func)
+                                          sigma_ratio=sigma_factor, sampler_opt=sampler_opt, noise=noise, scheduler_func=scheduler_func)
    else:
        advanced_steps = math.floor(steps / denoise)
        start_at_step = advanced_steps - steps
@@ -251,7 +215,7 @@ def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive,
        # print(f"pre: {start_at_step} .. {end_at_step} / {advanced_steps}")
        temp_latent = separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler,
                                       positive, negative, latent_image, start_at_step, end_at_step, True,
-                                       sigma_ratio=sigma_factor, noise=noise, scheduler_func=scheduler_func)
+                                       sigma_ratio=sigma_factor, sampler_opt=sampler_opt, noise=noise, scheduler_func=scheduler_func)

        if 'noise_mask' in latent_image:
            # noise_latent = \
@@ -265,7 +229,7 @@ def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive,
        # print(f"post: {end_at_step} .. {advanced_steps + 1} / {advanced_steps}")
        refined_latent = separated_sample(refiner_model, False, seed, advanced_steps, cfg, sampler_name, scheduler,
                                          refiner_positive, refiner_negative, temp_latent, end_at_step, advanced_steps + 1, False,
-                                          sigma_ratio=sigma_factor, scheduler_func=scheduler_func)
+                                          sigma_ratio=sigma_factor, sampler_opt=sampler_opt, scheduler_func=scheduler_func)

    return refined_latent

@@ -17,7 +17,6 @@ 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
@@ -183,7 +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})


@@ -512,7 +511,7 @@ def onprompt_populate_wildcards(json_data):
                else:
                    input_seed = int(inputs['seed'])

-                inputs['populated_text'] = wildcards.process(inputs['wildcard_text'], input_seed)
+                inputs['populated_text'] = impact.wildcards.process(inputs['wildcard_text'], input_seed)
                inputs['mode'] = 'reproduce'

                PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "populated_text", "type": "STRING", "value": inputs['populated_text']})
@@ -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
@@ -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,
@@ -174,6 +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):

@@ -500,7 +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}),
@@ -513,51 +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:
@@ -625,6 +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):
@@ -1486,6 +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:
@@ -1542,6 +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):
@@ -178,31 +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


@@ -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), count=1) for x in selected_items]
            replacement = select_sep.join(selected_items2)
            if '::' in replacement:
                pass
@@ -194,7 +220,7 @@ def process(text, seed=None):
            replacements_found = True
            return replacement

-        pattern = r'{([^{}]*?)}'
+        pattern = r'(?<!\\)\{((?:[^{}]|(?<=\\)[{}])*?)(?<!\\)\}'
        replaced_string = re.sub(pattern, replace_option, string)

        return replaced_string, replacements_found
@@ -253,7 +279,7 @@ def process(text, seed=None):

                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)
+                replacement = re.sub(r'^\s*[0-9.]+::', '', selected_item, count=1)
                replacements_found = True
                string = string.replace(f"__{match}__", replacement, 1)
            elif '*' in keyword:
@@ -333,6 +359,7 @@ def extract_lora_values(string):
        lbw = None
        lbw_a = None
        lbw_b = None
+        loader = None

        if len(item) > 0:
            lora = item[0]
@@ -351,6 +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
@@ -358,7 +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
@@ -382,6 +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):
    """
@@ -402,7 +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"
@@ -416,26 +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 != '']
@@ -444,7 +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 = "8.6"
+version = "8.17.1"
 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	705698faf2	bump version	2025-06-19 12:40:42 +09:00
Dr.Lt.Data	cf43fc4e3c	fix: typo in locales	2025-06-19 12:39:30 +09:00
robo	4c292e684e	change wildcard {} regex to allow escaping (#968 )	2025-06-19 12:39:06 +09:00
Dr.Lt.Data	65f1363a10	feat: LamaRemoverDetailerHookProvider is added	2025-06-17 23:40:50 +09:00
Dr.Lt.Data	f57d309932	fixed: avoid potential conflict - If everything is working as expected, this conflict shouldn't occur. However, some node pack might be adding the `impact` module to `sys.path`. https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/1022	2025-06-17 00:46:43 +09:00
NicolasKlenert	6ccf9bab68	fixed: SegmDetectorCombined - incorrect empty mask dimension (#1021 )	2025-06-16 12:34:56 +09:00
Dr.Lt.Data	ac3668d946	feat: CustomSamplerDetailerHookProvider is added https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/957	2025-06-11 21:52:25 +09:00
Emmanuel Ferdman	78d3793a77	Resolve regex library warnings (#1011 ) Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>	2025-06-10 12:35:46 +09:00
Dr.Lt.Data	2346b67766	fixed: Switch (Any) - copy&paste error https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/1012	2025-06-01 04:52:24 +09:00
Dr.Lt.Data	025db4b581	fixed: MaskRectArea - type error https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/1001	2025-06-01 04:43:46 +09:00
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