fixed: impact switch - compatibility patch with new front

https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/964
refactor: remove useless legacy code
2025-04-22 02:06:08 +09:00 · 2025-04-22 01:33:58 +09:00 · 2025-04-13 10:24:35 +09:00 · 2025-04-12 19:01:00 +09:00 · 2025-03-24 00:06:42 +09:00 · 2025-03-24 00:06:06 +09:00
35 changed files with 10301 additions and 303 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.
@@ -224,7 +251,7 @@ NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pac


 ### Impact KSampler
-  * These samplers support basic_pipe and AYS scheduler
+  * These samplers support basic_pipe and AYS/OSS/GITS scheduler
  * `KSampler (pipe)` - pipe version of KSampler
  * `KSampler (advanced/pipe)` - pipe version of KSamplerAdvacned
  * When converting the scheduler widget to input, refer to the `Impact Scheduler Adapter` node to resolve compatibility issues.
@@ -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
@@ -248,6 +247,8 @@ NODE_CLASS_MAPPINGS = {
    "ImpactSEGSLabelFilter": SEGSLabelFilter,
    "ImpactSEGSRangeFilter": SEGSRangeFilter,
    "ImpactSEGSOrderedFilter": SEGSOrderedFilter,
+    "ImpactSEGSIntersectionFilter": SEGSIntersectionFilter,
+    "ImpactSEGSNMSFilter": SEGSNMSFilter,

    "ImpactCompare": ImpactCompare,
    "ImpactConditionalBranch": ImpactConditionalBranch,
@@ -363,6 +364,8 @@ 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",
@@ -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
+}
@@ -1,35 +0,0 @@
-import { ComfyApp, app } from "../../scripts/app.js";
-
-let conflict_check = undefined;
-
-app.registerExtension({
-	name: "Comfy.impact.comboBoolMigration",
-
-	nodeCreated(node, app) {
-		for(let i in node.widgets) {
-			let widget = node.widgets[i];
-
-			if(conflict_check == undefined) {
-				conflict_check = !!app.extensions.find((ext) => ext.name === "Comfy.comboBoolMigration");
-			}
-
-			if(conflict_check)
-				return;
-
-			if(widget.type == "toggle") {
-				let value = widget.value;
-
-				var v = Object.getOwnPropertyDescriptor(widget, 'value');
-				if(!v) {
-					Object.defineProperty(widget, "value", {
-						set: (value) => {
-								delete widget.value;
-								widget.value = value == true || value == widget.options.on;
-							},
-						get: () => { return value; }
-					});
-				}
-			}
-		}
-	}
-});
@@ -3,6 +3,48 @@ import { app } from "../../scripts/app.js";

 let original_show = app.ui.dialog.show;

+export function customAlert(message) {
+	try {
+		app.extensionManager.toast.addAlert(message);
+	}
+	catch {
+		alert(message);
+	}
+}
+
+export function isBeforeFrontendVersion(compareVersion) {
+    try {
+        const frontendVersion = window['__COMFYUI_FRONTEND_VERSION__'];
+        if (typeof frontendVersion !== 'string') {
+            return false;
+        }
+
+        function parseVersion(versionString) {
+            const parts = versionString.split('.').map(Number);
+            return parts.length === 3 && parts.every(part => !isNaN(part)) ? parts : null;
+        }
+
+        const currentVersion = parseVersion(frontendVersion);
+        const comparisonVersion = parseVersion(compareVersion);
+
+        if (!currentVersion || !comparisonVersion) {
+            return false;
+        }
+
+        for (let i = 0; i < 3; i++) {
+            if (currentVersion[i] > comparisonVersion[i]) {
+                return false;
+            } else if (currentVersion[i] < comparisonVersion[i]) {
+                return true;
+            }
+        }
+
+        return false;
+    } catch {
+        return true;
+    }
+}
+
 function dialog_show_wrapper(html) {
 	if (typeof html === "string") {
 		if(html.includes("IMPACT-PACK-SIGNAL: STOP CONTROL BRIDGE")) {
@@ -1,6 +1,13 @@
 import { ComfyApp, app } from "../../scripts/app.js";
 import { ComfyDialog, $el } from "../../scripts/ui.js";
 import { api } from "../../scripts/api.js";
+import { customAlert, isBeforeFrontendVersion } from "./common.js";
+
+const is_legacy_front = () => isBeforeFrontendVersion('1.16.9');
+
+if(is_legacy_front()) {
+	customAlert("An outdated version(<1.16.9) of the `comfyui-frontend-package` is installed. It is not compatible with the current version of the Impact Pack.");
+}

 let wildcards_list = [];
 async function load_wildcards() {
@@ -93,7 +100,7 @@ const input_dirty = {};
 const output_tracking = {};

 function progressExecuteHandler(event) {
-	if(event.detail.output.aux){
+	if(event.detail?.output?.aux){
 		const id = event.detail.node;
 		if(input_tracking.hasOwnProperty(id)) {
 			if(input_tracking.hasOwnProperty(id) && input_tracking[id][0] != event.detail.output.aux[0]) {
@@ -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") {
@@ -273,7 +280,7 @@ app.registerExtension({
 				}
 				else {
 					const node = app.graph.getNodeById(link_info.origin_id);
-					slot_type = node.outputs[link_info.origin_slot].type;
+					slot_type = node.outputs[link_info.origin_slot]?.type;
 				}

 				this.inputs[0].type = slot_type;
@@ -365,7 +372,11 @@ app.registerExtension({
 					// connect input
 					if(this.inputs[0].type == '*'){
 						const node = app.graph.getNodeById(link_info.origin_id);
-						let origin_type = node.outputs[link_info.origin_slot].type;
+						let origin_type = node.outputs[link_info.origin_slot]?.type;
+
+						if(origin_type==undefined) {
+							return; // fallback
+						}

 						if(origin_type == '*') {
 							this.disconnectInput(link_info.target_slot);
@@ -399,6 +410,9 @@ app.registerExtension({
 				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++;
 				}

@@ -467,6 +481,11 @@ app.registerExtension({

 			const onConnectionsChange = nodeType.prototype.onConnectionsChange;
 			nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
+				const stackTrace = new Error().stack;
+				if(stackTrace.includes('LGraph.configure')) {
+					return;
+				}
+
 				if(!link_info)
 					return;

@@ -509,7 +528,7 @@ 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');
 						}
@@ -534,13 +553,7 @@ 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
@@ -550,6 +563,7 @@ app.registerExtension({
 					}
 				}

+
 				let slot_i = 1;
 				for (let i = 0; i < this.inputs.length; i++) {
 					let input_i = this.inputs[i];
@@ -560,11 +574,7 @@ 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);
-				}
+				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;
@@ -613,12 +623,14 @@ 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", {
@@ -692,18 +704,20 @@ 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"; }
 			});

@@ -716,14 +730,16 @@ 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;
+						}
 					}
 				}

@@ -755,14 +771,20 @@ app.registerExtension({
 			// mode combo
 			Object.defineProperty(mode_widget, "value", {
 				set: (value) => {
-						node._mode_value = value == true || value == "Populate";
-						populated_text_widget.inputEl.disabled = value == true || value == "Populate";
+						if(value == true)
+							node._mode_value = "populate";
+						else if(value == false)
+							node._mode_value = "fixed";
+						else
+							node._mode_value = value; // combo value
+
+						populated_text_widget.inputEl.disabled = node._mode_value == 'populate';
 					},
 				get: () => {
 						if(node._mode_value != undefined)
 							return node._mode_value;
 						else
-							return true;
+							return 'populate';
 					 }
 			});
 		}
@@ -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, 3, 1]
+version_code = [8, 12]
 version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')

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

 current_prompt = None

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


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

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

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

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

    if detailer_hook is not None:
        refined_image = detailer_hook.post_decode(refined_image)
@@ -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."}),
@@ -218,6 +222,8 @@ class DetailerForEach:
                    "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                    "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
                    "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                    "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                    "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                   }
                }

@@ -226,6 +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
@@ -234,7 +242,7 @@ class DetailerForEach:
    def do_detail(image, segs, model, clip, vae, guide_size, guide_size_for_bbox, max_size, seed, steps, cfg, sampler_name, scheduler,
                  positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard_opt=None, detailer_hook=None,
                  refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None,
-                  cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+                  cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):

        if len(image) > 1:
            raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -338,7 +346,8 @@ class DetailerForEach:
                                                                refiner_clip=refiner_clip, refiner_positive=refiner_positive,
                                                                refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper,
                                                                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,
-                                                                scheduler_func=scheduler_func_opt)
+                                                                scheduler_func=scheduler_func_opt, vae_tiled_encode=tiled_encode,
+                                                                vae_tiled_decode=tiled_decode)
            else:
                enhanced_image = cropped_image
                cnet_pils = None
@@ -384,13 +393,15 @@ class DetailerForEach:

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

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

        return (enhanced_img, )

@@ -425,6 +436,8 @@ class DetailerForEachPipe:
                      "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                      "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
                      "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                      "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                      "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                     }
                }

@@ -435,10 +448,13 @@ class DetailerForEachPipe:

    CATEGORY = "ImpactPack/Detailer"

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

        if len(image) > 1:
            raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -456,7 +472,8 @@ class DetailerForEachPipe:
                                      force_inpaint, wildcard, detailer_hook,
                                      refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                                      refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
-                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt,
+                                      tiled_encode=tiled_encode, tiled_decode=tiled_decode)

        # set fallback image
        if len(cnet_pil_list) == 0:
@@ -513,6 +530,8 @@ class FaceDetailer:
                    "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                    "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
                    "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                    "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                    "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                }}

    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK", "DETAILER_PIPE", "IMAGE")
@@ -522,6 +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,
@@ -530,7 +551,7 @@ class FaceDetailer:
                     sam_mask_hint_use_negative, drop_size,
                     bbox_detector, segm_detector=None, sam_model_opt=None, wildcard_opt=None, detailer_hook=None,
                     refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, cycle=1,
-                     inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+                     inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):

        # make default prompt as 'face' if empty prompt for CLIPSeg
        bbox_detector.setAux('face')
@@ -562,7 +583,8 @@ class FaceDetailer:
                                          refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                                          refiner_clip=refiner_clip, refiner_positive=refiner_positive,
                                          refiner_negative=refiner_negative,
-                                          cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                          cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,
+                                          scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode)
        else:
            enhanced_img = image
            cropped_enhanced = []
@@ -588,7 +610,8 @@ class FaceDetailer:
             bbox_threshold, bbox_dilation, bbox_crop_factor,
             sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
             sam_mask_hint_use_negative, drop_size, bbox_detector, wildcard, cycle=1,
-             sam_model_opt=None, segm_detector_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             sam_model_opt=None, segm_detector_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0,
+             scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):

        result_img = None
        result_mask = None
@@ -606,7 +629,8 @@ class FaceDetailer:
                bbox_threshold, bbox_dilation, bbox_crop_factor,
                sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
                sam_mask_hint_use_negative, drop_size, bbox_detector, segm_detector_opt, sam_model_opt, wildcard, detailer_hook,
-                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt,
+                tiled_encode=tiled_encode, tiled_decode=tiled_decode)

            result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img
            result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask
@@ -1381,6 +1405,8 @@ class FaceDetailerPipe:
                    "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                    "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
                    "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                    "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                    "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                   }
                }

@@ -1391,11 +1417,14 @@ class FaceDetailerPipe:

    CATEGORY = "ImpactPack/Simple"

+    DESCRIPTION = FaceDetailer.DESCRIPTION
+
    def doit(self, image, detailer_pipe, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
             denoise, feather, noise_mask, force_inpaint, bbox_threshold, bbox_dilation, bbox_crop_factor,
             sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion,
             sam_mask_hint_threshold, sam_mask_hint_use_negative, drop_size, refiner_ratio=None,
-             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None,
+             tiled_encode=False, tiled_decode=False):

        result_img = None
        result_mask = None
@@ -1418,7 +1447,8 @@ class FaceDetailerPipe:
                sam_mask_hint_use_negative, drop_size, bbox_detector, segm_detector, sam_model_opt, wildcard, detailer_hook,
                refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
-                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt,
+                tiled_encode=tiled_encode, tiled_decode=tiled_decode)

            result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img
            result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask
@@ -1484,6 +1514,8 @@ class MaskDetailerPipe:

    CATEGORY = "ImpactPack/Detailer"

+    DESCRIPTION = ""
+
    def doit(self, image, mask, basic_pipe, guide_size, guide_size_for, max_size, mask_mode,
             seed, steps, cfg, sampler_name, scheduler, denoise,
             feather, crop_factor, drop_size, refiner_ratio, batch_size, cycle=1,
@@ -1552,7 +1584,7 @@ class DetailerForEachTest(DetailerForEach):

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

        if len(image) > 1:
            raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -1561,7 +1593,8 @@ class DetailerForEachTest(DetailerForEach):
            DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps,
                                      cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
                                      force_inpaint, wildcard, detailer_hook,
-                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, 
+                                      scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode)

        # set fallback image
        if len(cropped) == 0:
@@ -1588,9 +1621,12 @@ class DetailerForEachTestPipe(DetailerForEachPipe):

    CATEGORY = "ImpactPack/Detailer"

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

        if len(image) > 1:
            raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -1609,7 +1645,8 @@ class DetailerForEachTestPipe(DetailerForEachPipe):
                                      refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                                      refiner_clip=refiner_clip, refiner_positive=refiner_positive,
                                      refiner_negative=refiner_negative,
-                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,
+                                      scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode)

        # set fallback image
        if len(cropped) == 0:
@@ -2322,7 +2359,11 @@ class ImpactWildcardProcessor:
        return {"required": {
                        "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "Enter a prompt using wildcard syntax."}),
                        "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "The actual value passed during the execution of 'ImpactWildcardProcessor' is what is shown here. The behavior varies slightly depending on the mode. Wildcard syntax can also be used in 'populated_text'."}),
-                        "mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed", "tooltip": "Populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\nFixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode."}),
+                        "mode": (["populate", "fixed", "reproduce"], {"default": "populate", "tooltip":
+                            "populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n"
+                            "fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode.\n"
+                            "reproduce: This mode operates as 'fixed' mode only once for reproduction, and then it switches to 'populate' mode."
+                            }),
                        "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Determines the random seed to be used for wildcard processing."}),
                        "Select to add Wildcard": (["Select the Wildcard to add to the text"],),
                    },
@@ -2331,9 +2372,10 @@ class ImpactWildcardProcessor:
    CATEGORY = "ImpactPack/Prompt"

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

    RETURN_TYPES = ("STRING", )
+    RETURN_NAMES = ("processed text",)
    FUNCTION = "doit"

    @staticmethod
@@ -2353,8 +2395,10 @@ class ImpactWildcardEncode:
                        "clip": ("CLIP",),
                        "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "Enter a prompt using wildcard syntax."}),
                        "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "The actual value passed during the execution of 'ImpactWildcardEncode' is what is shown here. The behavior varies slightly depending on the mode. Wildcard syntax can also be used in 'populated_text'."}),
-                        "mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed", "tooltip": "Populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n"
-                                                                                                                       "Fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode."}),
+                        "mode": (["populate", "fixed", "reproduce"], {"tooltip": 
+                            "populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n"
+                            "fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode\n."
+                            "reproduce: This mode operates as 'fixed' mode only once for reproduction, and then it switches to 'populate' mode."}),
                        "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"), ),
                        "Select to add Wildcard": (["Select the Wildcard to add to the text"], ),
                        "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Determines the random seed to be used for wildcard processing."}),
@@ -2364,7 +2408,7 @@ class ImpactWildcardEncode:
    CATEGORY = "ImpactPack/Prompt"

    DESCRIPTION = ("The 'ImpactWildcardEncode' node processes text prompts written in wildcard syntax and outputs them as conditioning. It also supports LoRA syntax, with the applied LoRA reflected in the model's output.\n\n"
-                   "TIP1: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'Fixed'.\n"
+                   "TIP1: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'fixed'.\n"
                   "TIP2: If the 'Inspire Pack' is installed, LBW(LoRA Block Weight) syntax can also be applied.")

    RETURN_TYPES = ("MODEL", "CLIP", "CONDITIONING", "STRING")
@@ -2391,7 +2435,7 @@ class ImpactSchedulerAdapter:
    def INPUT_TYPES(s):
        return {"required": {
            "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"defaultInput": True, }),
-            "extra_scheduler": (['None', 'AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]', 'LTXV[default]'],),
+            "extra_scheduler": (['None', 'AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]', 'LTXV[default]', 'OSS FLUX', 'OSS Wan'],),
        }}

    CATEGORY = "ImpactPack/Util"
@@ -29,6 +29,8 @@ def calculate_sigmas(model, sampler, scheduler, steps):
        sigmas = nodes.NODE_CLASS_MAPPINGS['GITSScheduler']().get_sigmas(float(scheduler[11:-1]), steps, denoise=1.0)[0]
    elif scheduler == 'LTXV[default]':
        sigmas = nodes.NODE_CLASS_MAPPINGS['LTXVScheduler']().get_sigmas(20, 2.05, 0.95, True, 0.1)[0]
+    elif scheduler.startswith('OSS'):
+        sigmas = nodes.NODE_CLASS_MAPPINGS['OptimalStepsScheduler']().get_sigmas(scheduler[4:], steps, denoise=1.0)[0]
    else:
        sigmas = samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, steps)

@@ -46,65 +48,27 @@ def get_noise_sampler(x, cpu, total_sigmas, **kwargs):


 def ksampler(sampler_name, total_sigmas, extra_options={}, inpaint_options={}):
-    if sampler_name == "dpmpp_sde":
-        def sample_dpmpp_sde(model, x, sigmas, **kwargs):
-            noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
-            if noise_sampler is not None:
-                kwargs['noise_sampler'] = noise_sampler
+    if sampler_name in ["dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu"]:
+        if sampler_name == "dpmpp_sde":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_sde
+        elif sampler_name == "dpmpp_sde_gpu":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_sde_gpu
+        elif sampler_name == "dpmpp_2m_sde":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_2m_sde
+        elif sampler_name == "dpmpp_2m_sde_gpu":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_2m_sde_gpu
+        elif sampler_name == "dpmpp_3m_sde":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_3m_sde
+        elif sampler_name == "dpmpp_3m_sde_gpu":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_3m_sde_gpu

-            return k_diffusion_sampling.sample_dpmpp_sde(model, x, sigmas, **kwargs)
+        def sampler_function_wrapper(model, x, sigmas, **kwargs):
+            if 'noise_sampler' not in kwargs:
+                kwargs['noise_sampler'] = get_noise_sampler(x, 'gpu' not in sampler_name, total_sigmas, **kwargs)

-        sampler_function = sample_dpmpp_sde
+            return orig_sampler_function(model, x, sigmas, **kwargs)

-    elif sampler_name == "dpmpp_sde_gpu":
-        def sample_dpmpp_sde(model, x, sigmas, **kwargs):
-            noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
-            if noise_sampler is not None:
-                kwargs['noise_sampler'] = noise_sampler
-
-            return k_diffusion_sampling.sample_dpmpp_sde_gpu(model, x, sigmas, **kwargs)
-
-        sampler_function = sample_dpmpp_sde
-
-    elif sampler_name == "dpmpp_2m_sde":
-        def sample_dpmpp_sde(model, x, sigmas, **kwargs):
-            noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
-            if noise_sampler is not None:
-                kwargs['noise_sampler'] = noise_sampler
-
-            return k_diffusion_sampling.sample_dpmpp_2m_sde(model, x, sigmas, **kwargs)
-
-        sampler_function = sample_dpmpp_sde
-
-    elif sampler_name == "dpmpp_2m_sde_gpu":
-        def sample_dpmpp_sde(model, x, sigmas, **kwargs):
-            noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
-            if noise_sampler is not None:
-                kwargs['noise_sampler'] = noise_sampler
-
-            return k_diffusion_sampling.sample_dpmpp_2m_sde_gpu(model, x, sigmas, **kwargs)
-
-        sampler_function = sample_dpmpp_sde
-
-    elif sampler_name == "dpmpp_3m_sde":
-        def sample_dpmpp_sde(model, x, sigmas, **kwargs):
-            noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
-            if noise_sampler is not None:
-                kwargs['noise_sampler'] = noise_sampler
-
-            return k_diffusion_sampling.sample_dpmpp_3m_sde(model, x, sigmas, **kwargs)
-
-        sampler_function = sample_dpmpp_sde
-
-    elif sampler_name == "dpmpp_3m_sde_gpu":
-        def sample_dpmpp_sde(model, x, sigmas, **kwargs):
-            noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
-            if noise_sampler is not None:
-                kwargs['noise_sampler'] = noise_sampler
-
-            return k_diffusion_sampling.sample_dpmpp_3m_sde_gpu(model, x, sigmas, **kwargs)
-
-        sampler_function = sample_dpmpp_sde
+        sampler_function = sampler_function_wrapper

    else:
        return comfy.samplers.sampler_object(sampler_name)
@@ -78,9 +78,13 @@ async def sam_prepare(request):
        if data['sam_model_name'] == 'auto':
            model_name = impact.config.get_config()['sam_editor_model']

-        model_name = os.path.join(impact_pack.model_path, "sams", model_name)
+        model_path = folder_paths.get_full_path("sams", model_name)

-        logging.info(f"[Impact Pack] Loading SAM model '{impact_pack.model_path}'")
+        if model_path is None:
+            logging.error(f"[Impact Pack] The '{model_name}' model file cannot be found in any sams model path.")
+            return web.Response(status=400)
+
+        logging.info(f"[Impact Pack] Loading SAM model '{model_path}'")

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

@@ -93,7 +97,7 @@ async def sam_prepare(request):
        if image_dir is None:
            return web.Response(status=400)

-        thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_name, filename,))
+        thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_path, filename,))
        thread.start()

        logging.info("[Impact Pack] SAM model loaded. ")
@@ -478,7 +482,17 @@ def onprompt_populate_wildcards(json_data):
    for k, v in prompt.items():
        if 'class_type' in v and (v['class_type'] == 'ImpactWildcardEncode' or v['class_type'] == 'ImpactWildcardProcessor'):
            inputs = v['inputs']
-            if inputs['mode'] and isinstance(inputs['populated_text'], str):
+
+            # legacy adapter
+            if isinstance(inputs['mode'], bool):
+                if inputs['mode']:
+                    new_mode = 'populate'
+                else:
+                    new_mode = 'fixed'
+
+                inputs['mode'] = new_mode
+
+            if inputs['mode'] == 'populate' and isinstance(inputs['populated_text'], str):
                if isinstance(inputs['seed'], list):
                    try:
                        input_node = prompt[inputs['seed'][0]]
@@ -499,17 +513,22 @@ def onprompt_populate_wildcards(json_data):
                    input_seed = int(inputs['seed'])

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

                PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "populated_text", "type": "STRING", "value": inputs['populated_text']})
                updated_widget_values[k] = inputs['populated_text']
+            
+            if inputs['mode'] == 'reproduce':
+                PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "mode", "type": "STRING", "value": 'populate'})
+
+

    if 'extra_data' in json_data and 'extra_pnginfo' in json_data['extra_data']:
        for node in json_data['extra_data']['extra_pnginfo']['workflow']['nodes']:
            key = str(node['id'])
            if key in updated_widget_values:
                node['widgets_values'][1] = updated_widget_values[key]
-                node['widgets_values'][2] = False
+                node['widgets_values'][2] = 'reproduce'


 def onprompt_for_remote(json_data):
@@ -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 = []
@@ -7,6 +7,7 @@ import nodes
 from . import config
 from PIL import Image
 import comfy
+import time


 class TensorBatchBuilder:
@@ -501,15 +502,21 @@ def crop_image(image, crop_region):
    return crop_tensor4(image, crop_region)


-def to_latent_image(pixels, vae):
+def to_latent_image(pixels, vae, vae_tiled_encode=False):
    x = pixels.shape[1]
    y = pixels.shape[2]
    if pixels.shape[1] != x or pixels.shape[2] != y:
        pixels = pixels[:, :x, :y, :]

-    vae_encode = nodes.VAEEncode()
+    start = time.time()
+    if vae_tiled_encode:
+        encoded = nodes.VAEEncodeTiled().encode(vae, pixels, 512, overlap=64)[0] # using default settings
+        print(f"[Impact Pack] vae encoded (tiled) in {time.time() - start:.1f}s")
+    else:
+        encoded = nodes.VAEEncode().encode(vae, pixels)[0]
+        print(f"[Impact Pack] vae encoded in {time.time() - start:.1f}s")

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


 def empty_pil_tensor(w=64, h=64):
@@ -44,7 +44,9 @@ 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
@@ -135,6 +137,8 @@ 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 +149,32 @@ def process(text, seed=None):
                        x = int(a)
                        select_range = (x, x)

+                    # Expand wildcard path or return the string after $$
+                    def expand_wildcard_or_return_string(options, pattern, wildcard_pattern):
+                        matches = re.findall(wildcard_pattern, pattern)
+                        if len(options) == 1 and matches:
+                            # $$<single wildcard>
+                            return get_wildcard_options(pattern)
+                        else:
+                            # $$opt1|opt2|...
+                            options[0] = pattern
+                            return options
+
                    if select_range is not None and len(multi_select_pattern) == 2:
                        # PATTERN: count$$
-                        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 +188,30 @@ def process(text, seed=None):
            if select_range is None:
                select_count = 1
            else:
-                select_count = random_gen.integers(low=select_range[0], high=select_range[1]+1, size=1)
+                def calculate_max(_options_length, _max_select_range):
+                    return min(_max_select_range + 1, _options_length + 1) if _max_select_range > 0 else _options_length + 1

-            if select_count > len(options):
+                def calculate_select_count(_max_value, _min_select_range, random_gen):
+                    if max(_max_value, _min_select_range) <= 0:
+                        return 0
+                    # fix: low >= high
+                    elif _max_value == _min_select_range:
+                        return _max_value
+                    else:
+                        # fix: low >= high
+                        _low_value = min(_min_select_range, _max_value)
+                        _high_value = max(_min_select_range, _max_value)
+                        return random_gen.integers(low=_low_value, high=_high_value, size=1)
+                select_count = calculate_select_count(calculate_max(len(options), select_range[1]), select_range[0], random_gen)
+
+            if select_count > len(options) or total_prob <= 1:
                random_gen.shuffle(options)
                selected_items = options
            else:
                selected_items = random_gen.choice(options, p=normalized_probabilities, size=select_count, replace=False)

-            selected_items2 = [re.sub(r'^\s*[0-9.]+::', '', x, 1) for x in selected_items]
+            # x may be numpy.int32, convert to string
+            selected_items2 = [re.sub(r'^\s*[0-9.]+::', '', str(x), 1) for x in selected_items]
            replacement = select_sep.join(selected_items2)
            if '::' in replacement:
                pass
@@ -237,7 +262,23 @@ def process(text, seed=None):
            keyword = match.lower()
            keyword = wildcard_normalize(keyword)
            if keyword in local_wildcard_dict:
-                replacement = random_gen.choice(local_wildcard_dict[keyword])
+                # look for adjusted probability
+                adjusted_probabilities = []
+                total_prob = 0
+                options=local_wildcard_dict[keyword]
+                for option in options:
+                    parts = option.split('::', 1)
+                    if len(parts) == 2 and is_numeric_string(parts[0].strip()):
+                        config_value = float(parts[0].strip())
+                    else:
+                        config_value = 1  # Default value if no configuration is provided
+
+                    adjusted_probabilities.append(config_value)
+                    total_prob += config_value
+
+                normalized_probabilities = [prob / total_prob for prob in adjusted_probabilities]
+                selected_item = random_gen.choice(options, p=normalized_probabilities, replace=False)
+                replacement = re.sub(r'^\s*[0-9.]+::', '', selected_item, 1)
                replacements_found = True
                string = string.replace(f"__{match}__", replacement, 1)
            elif '*' in keyword:
@@ -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 = "8.3.1"
+version = "8.12"
 license = { file = "LICENSE.txt" }
 dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]
Author	SHA1	Message	Date
Dr.Lt.Data	5a2adda580	fixed: impact switch - compatibility patch with new front https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/964	2025-04-22 02:06:08 +09:00
Dr.Lt.Data	d05882a9e0	refactor: remove useless legacy code	2025-04-22 01:33:58 +09:00
Dr.Lt.Data	d900939861	feat: Impact Sampler - support OSS scheduler	2025-04-13 10:24:35 +09:00
Jaquan	cd2696f6fd	fix #946 InversedSwitch(Any) was not append outputs after first connect (#956 ) InversedSwitch(Any) not increasing number of outputs after connecting first output (in ) # 953 #946 In the latest ComfyUI_frontend version, output is not appended as expected	2025-04-12 19:01:00 +09:00
Dr.Lt.Data	0b1ac0f1c5	bump version to v8.10	2025-03-24 00:06:42 +09:00
chutchatut	216d7fd60c	Added sort=None to SEGS order filter (#944 ) * added pick first N SEGs node * remove take first N segs node and update segs order filter to add order=None * add comments * update for readibility * format code * remove match statement	2025-03-24 00:06:06 +09:00
chutchatut	28cd2f70b2	Update README.md (#943 )	2025-03-21 19:06:24 +09:00
Dr.Lt.Data	2708eba825	bump version v8.9	2025-03-20 21:36:25 +09:00
chutchatut	c19ab92172	add intersection and non max suppression SEGS filter (#940 )	2025-03-20 21:21:15 +09:00
Robin Huangandsnomiao	6e3d07277b	chore(publish): update workflow for node publishing with conditional execution and permissions (#935 ) Co-authored-by: snomiao <snomiao+comfy-pr@gmail.com>	2025-03-17 00:19:33 +09:00
Dr.Lt.Data	782c6f439e	fixed: controlbridge - better error message - mute/bypass behavior cannot be used in api mode https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/933	2025-03-12 18:10:42 +09:00
Dr.Lt.Data	0e3e6a193a	improved: SAM Loader - don't add 'ESAM' if 'ComfyUI-YoloWorld-EfficientSAM' is not installed	2025-03-04 22:44:31 +09:00
Dr.Lt.Data	798776838e	remove sample_error_enhancer	2025-03-04 12:43:03 +09:00
Dr.Lt.Data	66493d8cb4	formatting..	2025-03-02 16:56:59 +09:00
Dr.Lt.Data	b4fd0834e0	fixed: crash when loading api json https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/919	2025-03-02 16:54:16 +09:00
Dr.Lt.Data	808b0dedf0	update README.md	2025-02-23 10:03:04 +09:00
Dr.Lt.Data	c6056b132d	add example workflow	2025-02-15 11:04:03 +09:00
Dr.Lt.Data	1ae7cae2df	version marker	2025-02-02 15:05:41 +09:00
izmp	ccb6285548	Fixed several issues related to number handling and wildcard processing (#896 )	2025-02-02 15:04:57 +09:00
Dr.Lt.Data	092310bc8f	refactor: impact_sampling	2025-01-31 21:01:03 +09:00
Dr.Lt.Data	5c530eb32e	fixed: wildcards - cannot edit populated_text on 'fixed' mode https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/898	2025-01-30 16:55:01 +09:00
Dr.Lt.Data	e35bc23fd1	add locales partly. added: more descriptions added: locales/ko	2025-01-30 16:49:23 +09:00
Dr.Lt.Data	869ac6fd1f	version marker	2025-01-28 12:21:26 +09:00
Dijkstra	cb168d64ab	feat: allow wildcard file to use the adjusted probabilities feature (#891 )	2025-01-28 12:16:10 +09:00
Dr.Lt.Data	a89e9e01a6	fixed: sam editor - cannot load sam model properly when sams directory is specified manually. https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/895	2025-01-28 06:35:51 +09:00
Dr.Lt.Data	e70b4df9b5	fixed: `reproduce` mode is reflected to the saved metadata but it wans't	2025-01-27 11:05:12 +09:00
Dr.Lt.Data	af1ef7e441	feat: wildcards nodes - `reproduce` mode is added. https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/882 fixed: js crash https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/886#issuecomment-2613922083 removed: combo bool migration script	2025-01-27 10:55:47 +09:00
Dr.Lt.Data	d8738eee2f	hotfix: front - robustness fix https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/886#issuecomment-2600716281	2025-01-19 18:08:59 +09:00
Dr.Lt.Data	c397c68ca3	version marker	2025-01-19 03:17:25 +09:00
Alex Butler	0e0722ec08	Add tiled vae encoding/decoding toggle to detailer nodes (#883 ) * detailers: support optional tiled vae encoding & decoding * Remove start encoding/decoding logging	2025-01-19 03:16:56 +09:00