fixed: sam detector - invalid position

https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/853

.github/workflows/publish.yml

@@ -0,0 +1,21 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

README.md

@@ -2,11 +2,19 @@
 
 # ComfyUI-Impact-Pack
 
-**Custom nodes pack for ComfyUI**
-This custom node helps to conveniently enhance images through Detector, Detailer, Upscaler, Pipe, and more.
+**Custom node pack for ComfyUI**
+This node pack helps to conveniently enhance images through Detector, Detailer, Upscaler, Pipe, and more.
 
+NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pack. To use the UltralyticsDetectorProvider node, please install the ComfyUI-Impact-Subpack separately.
 
 ## NOTICE 
+* V8.0: The `Impact Subpack` is no longer installed automatically. To use `UltralyticsDetectorProvider` nodes, please install the `Impact Subpack` separately.
+* V7.6: Automatic installation is no longer supported. Please install using ComfyUI-Manager, or manually install requirements.txt and run install.py to complete the installation.
+* V7.0: Supports Switch based on Execution Model Inversion.
+* V6.0: Supports FLUX.1 model in Impact KSampler, Detailers, PreviewBridgeLatent
+* V5.0: It is no longer compatible with versions of ComfyUI before 2024.04.08. 
+* V4.87.4: Update to a version of ComfyUI after 2024.04.08 for proper functionality.
+* V4.85: Incompatible with the outdated **ComfyUI IPAdapter Plus**. (A version dated March 24th or later is required.)
 * V4.77: Compatibility patch applied. Requires ComfyUI version (Oct. 8th) or later.
 * V4.73.3: ControlNetApply (SEGS) supports AnimateDiff
 * V4.20.1: Due to the feature update in `RegionalSampler`, the parameter order has changed, causing malfunctions in previously created `RegionalSamplers`. Please adjust the parameters accordingly.
@@ -25,220 +33,239 @@ This custom node helps to conveniently enhance images through Detector, Detailer
 
 
 ## Custom Nodes
-* [Detectors](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/detectors.md)
-  * SAMLoader - Loads the SAM model.
-  * UltralyticsDetectorProvider - Loads the Ultralystics model to provide SEGM_DETECTOR, BBOX_DETECTOR.
-    - Unlike `MMDetDetectorProvider`, for segm models, `BBOX_DETECTOR` is also provided.
-    - The various models available in UltralyticsDetectorProvider can be downloaded through **ComfyUI-Manager**.
-  * ONNXDetectorProvider - Loads the ONNX model to provide BBOX_DETECTOR.
-  * CLIPSegDetectorProvider - Wrapper for CLIPSeg to provide BBOX_DETECTOR.
+### [Detector nodes](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/detectors.md)
+  * `SAMLoader` - Loads the SAM model.
+  * `ONNXDetectorProvider` - Loads the ONNX model to provide BBOX_DETECTOR.
+  * `CLIPSegDetectorProvider` - Wrapper for CLIPSeg to provide BBOX_DETECTOR.
     * You need to install the ComfyUI-CLIPSeg node extension.
-  * SEGM Detector (combined) - Detects segmentation and returns a mask from the input image.
-  * BBOX Detector (combined) - Detects bounding boxes and returns a mask from the input image.
-  * SAMDetector (combined) - Utilizes the SAM technology to extract the segment at the location indicated by the input SEGS on the input image and outputs it as a unified mask.
-  * SAMDetector (Segmented) - It is similar to `SAMDetector (combined)`, but it separates and outputs the detected segments. Multiple segments can be found for the same detected area, and currently, a policy is in place to group them arbitrarily in sets of three. This aspect is expected to be improved in the future.
+  * `SEGM Detector (combined)` - Detects segmentation and returns a mask from the input image.
+  * `BBOX Detector (combined)` - Detects bounding boxes and returns a mask from the input image.
+  * `SAMDetector (combined)` - Utilizes the SAM technology to extract the segment at the location indicated by the input SEGS on the input image and outputs it as a unified mask.
+  * `SAMDetector (Segmented)` - It is similar to `SAMDetector (combined)`, but it separates and outputs the detected segments. Multiple segments can be found for the same detected area, and currently, a policy is in place to group them arbitrarily in sets of three. This aspect is expected to be improved in the future.
     * As a result, it outputs the `combined_mask`, which is a unified mask, and `batch_masks`, which are multiple masks grouped together in batch form.
     * While `batch_masks` may not be completely separated, it provides functionality to perform some level of segmentation.
-  * Simple Detector (SEGS) - Operating primarily with `BBOX_DETECTOR`, and with the additional provision of `SAM_MODEL` or `SEGM_DETECTOR`, this node internally generates improved SEGS through mask operations on both *bbox* and *silhouette*. It serves as a convenient tool to simplify a somewhat intricate workflow.
+  * `Simple Detector (SEGS)` - Operating primarily with `BBOX_DETECTOR`, and with the additional provision of `SAM_MODEL` or `SEGM_DETECTOR`, this node internally generates improved SEGS through mask operations on both *bbox* and *silhouette*. It serves as a convenient tool to simplify a somewhat intricate workflow.
 
-* ControlNet, IPAdapter
-  * ControlNetApply (SEGS) - To apply ControlNet in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node.
-    * `segs_preprocessor` and `control_image` can be selectively applied. If an `control_image` is given, `segs_preprocessor` will be ignored.
+### ControlNet, IPAdapter
+  * `ControlNetApply (SEGS)` - To apply ControlNet in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node.
+    * `segs_preprocessor` and `control_image` can be selectively applied. If a `control_image` is given, `segs_preprocessor` will be ignored.
     * If set to `control_image`, you can preview the cropped cnet image through `SEGSPreview (CNET Image)`. Images generated by `segs_preprocessor` should be verified through the `cnet_images` output of each Detailer.
     * The `segs_preprocessor` operates by applying preprocessing on-the-fly based on the cropped image during the detailing process, while `control_image` will be cropped and used as input to `ControlNetApply (SEGS)`.
-  * ControlNetClear (SEGS) - Clear applied ControlNet in SEGS
-  * IPAdapterApply (SEGS) - To apply IPAdapter in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node.
+  * `ControlNetClear (SEGS)` - Clear applied ControlNet in SEGS
+  * `IPAdapterApply (SEGS)` - To apply IPAdapter in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node.
 
-* Bitwise(SEGS & SEGS) - Performs a 'bitwise and' operation between two SEGS.
-* Bitwise(SEGS - SEGS) - Subtracts one SEGS from another.
-* Bitwise(SEGS & MASK) - Performs a bitwise AND operation between SEGS and MASK.
-* Bitwise(SEGS & MASKS ForEach) - Performs a bitwise AND operation between SEGS and MASKS.
-  * Please note that this operation is performed with batches of MASKS, not just a single MASK.
-* Bitwise(MASK & MASK) - Performs a 'bitwise and' operation between two masks.
-* Bitwise(MASK - MASK) - Subtracts one mask from another.
-* Bitwise(MASK + MASK) - Combine two masks.
-* SEGM Detector (SEGS) - Detects segmentation and returns SEGS from the input image.
-* BBOX Detector (SEGS) - Detects bounding boxes and returns SEGS from the input image.
+### Mask operation
+  * `Pixelwise(SEGS & SEGS)` - Performs a 'pixelwise and' operation between two SEGS.
+  * `Pixelwise(SEGS - SEGS)` - Subtracts one SEGS from another.
+  * `Pixelwise(SEGS & MASK)` - Performs a pixelwise AND operation between SEGS and MASK.
+  * `Pixelwise(SEGS & MASKS ForEach)` - Performs a pixelwise AND operation between SEGS and MASKS.
+    * Please note that this operation is performed with batches of MASKS, not just a single MASK.
+  * `Pixelwise(MASK & MASK)` - Performs a 'pixelwise and' operation between two masks.
+  * `Pixelwise(MASK - MASK)` - Subtracts one mask from another.
+  * `Pixelwise(MASK + MASK)` - Combine two masks.
+  * `SEGM Detector (SEGS)` - Detects segmentation and returns SEGS from the input image.
+  * `BBOX Detector (SEGS)` - Detects bounding boxes and returns SEGS from the input image.
+  * `Dilate Mask` - Dilate Mask.
+    * Support erosion for negative value.
+  * `Gaussian Blur Mask` - Apply Gaussian Blur to Mask. You can utilize this for mask feathering.
+  * `Mask Rect Area` - Create a rectangular mask defined by percentages with preview canvas.
+  * `Mask Rect Area (Advanced)` - Create a rectangular mask defined by pixels and image size. 
 
-* Detailer
-  * Detailer (SEGS) - Refines the image based on SEGS.
-  * DetailerDebug (SEGS) - Refines the image based on SEGS. Additionally, it provides the ability to monitor the cropped image and the refined image of the cropped image.
+### [Detailer nodes](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/detailers.md)
+  * `Detailer (SEGS)` - Refines the image based on SEGS.
+  * `DetailerDebug (SEGS)` - Refines the image based on SEGS. Additionally, it provides the ability to monitor the cropped image and the refined image of the cropped image.
     * To prevent regeneration caused by the seed that does not change every time when using 'external_seed', please disable the 'seed random generate' option in the 'Detailer...' node.
-  * MASK to SEGS - Generates SEGS based on the mask.
-    * MASK to SEGS For AnimateDiff - Generates SEGS based on the mask for AnimateDiff.
-  * MediaPipe FaceMesh to SEGS - Separate each landmark from the mediapipe facemesh image to create labeled SEGS.
+  * `MASK to SEGS` - Generates SEGS based on the mask.
+  * `MASK to SEGS For AnimateDiff` - Generates SEGS based on the mask for AnimateDiff.
+    * When using a single mask, convert it to SEGS to apply it to the entire frame.
+    * When using a batch mask, the contour fill feature is disabled.
+  * `MediaPipe FaceMesh to SEGS` - Separate each landmark from the mediapipe facemesh image to create labeled SEGS.
     * Usually, the size of images created through the MediaPipe facemesh preprocessor is downscaled. It resizes the MediaPipe facemesh image to the original size given as reference_image_opt for matching sizes during processing. 
-  * ToBinaryMask - Separates the mask generated with alpha values between 0 and 255 into 0 and 255. The non-zero parts are always set to 255.
-  * Masks to Mask List - This node converts the MASKS in batch form to a list of individual masks.
-  * Mask List to Masks - This node converts the MASK list to MASK batch form.
-  * EmptySEGS - Provides an empty SEGS.
-  * MaskPainter - Provides a feature to draw masks.
-  * FaceDetailer - Easily detects faces and improves them.
-  * FaceDetailer (pipe) - Easily detects faces and improves them (for multipass).
-  * MaskDetailer (pipe) - This is a simple inpaint node that applies the Detailer to the mask area.
+  * `ToBinaryMask` - Separates the mask generated with alpha values between 0 and 255 into 0 and 255. The non-zero parts are always set to 255.
+  * `Masks to Mask List` - This node converts the MASKS in batch form to a list of individual masks.
+  * `Mask List to Masks` - This node converts the MASK list to MASK batch form.
+  * `EmptySEGS` - Provides an empty SEGS.
+  * `MaskPainter` - Provides a feature to draw masks.
+  * `FaceDetailer` - Easily detects faces and improves them.
+  * `FaceDetailer (pipe)` - Easily detects faces and improves them (for multipass).
+  * `MaskDetailer (pipe)` - This is a simple inpaint node that applies the Detailer to the mask area.
 
-* `FromDetailer (SDXL/pipe), BasicPipe -> DetailerPipe (SDXL), Edit DetailerPipe (SDXL)` - These are pipe functions used in Detailer for utilizing the refiner model of SDXL.
+  * `FromDetailer (SDXL/pipe)`, `BasicPipe -> DetailerPipe (SDXL)`, `Edit DetailerPipe (SDXL)` - These are pipe functions used in Detailer for utilizing the refiner model of SDXL.
+  * `Any PIPE -> BasicPipe` - Convert the PIPE Value of other custom nodes that are not BASIC_PIPE but internally have the same structure as BASIC_PIPE to BASIC_PIPE. If an incompatible type is applied, it may cause runtime errors.
 
-* SEGS Manipulation nodes 
-  * SEGSDetailer - Performs detailed work on SEGS without pasting it back onto the original image.
-  * SEGSPaste - Pastes the results of SEGS onto the original image.
+### SEGS Manipulation nodes
+  * `SEGSDetailer` - Performs detailed work on SEGS without pasting it back onto the original image.
+  * `SEGSPaste` - Pastes the results of SEGS onto the original image.
     * If `ref_image_opt` is present, the images contained within SEGS are ignored. Instead, the image within `ref_image_opt` corresponding to the crop area of SEGS is taken and pasted. The size of the image in `ref_image_opt` should be the same as the original image size.
     * This node can be used in conjunction with the processing results of AnimateDiff.
-  * SEGSPreview - Provides a preview of SEGS.
+  * `SEGSPreview` - Provides a preview of SEGS.
      * This option is used to preview the improved image through `SEGSDetailer` before merging it into the original. Prior to going through ```SEGSDetailer```, SEGS only contains mask information without image information. If fallback_image_opt is connected to the original image, SEGS without image information will generate a preview using the original image. However, if SEGS already contains image information, fallback_image_opt will be ignored.
      * This node can be used in conjunction with the processing results of AnimateDiff.
-  * SEGSPreview (CNET Image) - Show images configured with `ControlNetApply (SEGS)` for debugging purposes.
-  * SEGSToImageList - Convert SEGS To Image List
-  * SEGSToMaskList - Convert SEGS To Mask List
-  * 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 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.
-  * Picker (SEGS) - Among the input SEGS, you can select a specific SEG through a dialog. If no SEG is selected, it outputs an empty SEGS. Increasing the batch_size of SEGSDetailer can be used for the purpose of selecting from the candidates.
-  * Set Default Image For SEGS - Set a default image for SEGS. SEGS with images set this way do not need to have a fallback image set. When override is set to false, the original image is preserved.
-  * Remove Image from SEGS - Remove the image set for the SEGS that has been configured by "Set Default Image for SEGS" or SEGSDetailer. When the image for the SEGS is removed, the Detailer node will operate based on the currently processed image instead of the SEGS. 
-  * Make Tile SEGS - [experimental] Create SEGS in the form of tiles from an image to facilitate experiments for Tiled Upscale using the Detailer.
+  * `SEGSPreview (CNET Image)` - Show images configured with `ControlNetApply (SEGS)` for debugging purposes.
+  * `SEGSToImageList` - Convert SEGS To Image List
+  * `SEGSToMaskList` - Convert SEGS To Mask List
+  * `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 Assign (label)` - Assign labels sequentially to SEGS. This node is useful when used with `[LAB]` of FaceDetailer.
+  * `SEGSConcat` - Concatenate segs1 and segs2. If source shape of segs1 and segs2 are different from segs2 will be ignored.
+  * `SEGS Merge` - SEGS contains multiple SEGs. SEGS Merge integrates several SEGs into a single merged SEG. The label is changed to `merged` and the confidence becomes the minimum confidence. The applied controlnet and cropped_image are removed.
+  * `Picker (SEGS)` - Among the input SEGS, you can select a specific SEG through a dialog. If no SEG is selected, it outputs an empty SEGS. Increasing the batch_size of SEGSDetailer can be used for the purpose of selecting from the candidates.
+  * `Set Default Image For SEGS` - Set a default image for SEGS. SEGS with images set this way do not need to have a fallback image set. When override is set to false, the original image is preserved.
+  * `Remove Image from SEGS` - Remove the image set for the SEGS that has been configured by "Set Default Image for SEGS" or SEGSDetailer. When the image for the SEGS is removed, the Detailer node will operate based on the currently processed image instead of the SEGS. 
+  * `Make Tile SEGS` - [experimental] Create SEGS in the form of tiles from an image to facilitate experiments for Tiled Upscale using the Detailer.
     * The `filter_in_segs_opt` and `filter_out_segs_opt` are optional inputs. If these inputs are provided, when creating the tiles, the mask for each tile is generated by overlapping with the mask of `filter_in_segs_opt` and excluding the overlap with the mask of `filter_out_segs_opt`. Tiles with an empty mask will not be created as SEGS.
-  * Dilate Mask (SEGS) - Dilate/Erosion Mask in SEGS
-  * Gaussian Blur Mask (SEGS) - Apply Gaussian Blur to Mask in SEGS
-  * SEGS_ELT Manipulation - experimental nodes
-    * DecomposeSEGS - Decompose SEGS to allow for detailed manipulation.
-    * AssembleSEGS - Reassemble the decomposed SEGS.
-    * From SEG_ELT - Extract detailed information from SEG_ELT.
-    * Edit SEG_ELT - Modify some of the information in SEG_ELT.
-    * Dilate SEG_ELT - Dilate the mask of SEG_ELT.
-
-* Mask Manipulation
-  * Dilate Mask - Dilate Mask. 
-    * Support erosion for negative value.
-  * Gaussian Blur Mask - Apply Gaussian Blur to Mask. You can utilize this for mask feathering.
+  * `Dilate Mask (SEGS)` - Dilate/Erosion Mask in SEGS
+  * `Gaussian Blur Mask (SEGS)` - Apply Gaussian Blur to Mask in SEGS
+  * `SEGS_ELT Manipulation` - experimental nodes
+    * `DecomposeSEGS` - Decompose SEGS to allow for detailed manipulation.
+    * `AssembleSEGS` - Reassemble the decomposed SEGS.
+    * `From SEG_ELT` - Extract detailed information from SEG_ELT.
+    * `Edit SEG_ELT` - Modify some of the information in SEG_ELT.
+    * `Dilate SEG_ELT` - Dilate the mask of SEG_ELT.
+    * `From SEG_ELT` bbox - Extract coordinate from bbox in SEG_ELT
+    * `From SEG_ELT` crop_region - Extract coordinate from crop_region in SEG_ELT
+  * `Count Elt in SEGS` - Number of Elts ins SEGS
  
-* Pipe nodes
-   * ToDetailerPipe, FromDetailerPipe - These nodes are used to bundle multiple inputs used in the detailer, such as models and vae, ..., into a single DETAILER_PIPE or extract the elements that are bundled in the DETAILER_PIPE.
-   * ToBasicPipe, FromBasicPipe - These nodes are used to bundle model, clip, vae, positive conditioning, and negative conditioning into a single BASIC_PIPE, or extract each element from the BASIC_PIPE.
-   * EditBasicPipe, EditDetailerPipe - These nodes are used to replace some elements in BASIC_PIPE or DETAILER_PIPE.
-   * FromDetailerPipe_v2, FromBasicPipe_v2 - It has the same functionality as `FromDetailerPipe` and `FromBasicPipe`, but it has an additional output that directly exports the input pipe. It is useful when editing EditBasicPipe and EditDetailerPipe.
-* Latent Scale (on Pixel Space) - This node converts latent to pixel space, upscales it, and then converts it back to latent.
+### Pipe nodes
+   * `ToDetailerPipe`, `FromDetailerPipe` - These nodes are used to bundle multiple inputs used in the detailer, such as models and vae, ..., into a single DETAILER_PIPE or extract the elements that are bundled in the DETAILER_PIPE.
+   * `ToBasicPipe`, `FromBasicPipe` - These nodes are used to bundle model, clip, vae, positive conditioning, and negative conditioning into a single BASIC_PIPE, or extract each element from the BASIC_PIPE.
+   * `EditBasicPipe`, `EditDetailerPipe` - These nodes are used to replace some elements in BASIC_PIPE or DETAILER_PIPE.
+   * `FromDetailerPipe_v2`, `FromBasicPipe_v2` - It has the same functionality as `FromDetailerPipe` and `FromBasicPipe`, but it has an additional output that directly exports the input pipe. It is useful when editing EditBasicPipe and EditDetailerPipe.
+* `Latent Scale (on Pixel Space)` - This node converts latent to pixel space, upscales it, and then converts it back to latent.
    * If upscale_model_opt is provided, it uses the model to upscale the pixel and then downscales it using the interpolation method provided in scale_method to the target resolution.
-* PixelKSampleUpscalerProvider - An upscaler is provided that converts latent to pixels using VAEDecode, performs upscaling, converts back to latent using VAEEncode, and then performs k-sampling. This upscaler can be attached to nodes such as 'Iterative Upscale' for use.
-  * Similar to 'Latent Scale (on Pixel Space)', if upscale_model_opt is provided, it performs pixel upscaling using the model.
-* PixelTiledKSampleUpscalerProvider - It is similar to PixelKSampleUpscalerProvider, but it uses ComfyUI_TiledKSampler and Tiled VAE Decoder/Encoder to avoid GPU VRAM issues at high resolutions.
+* `PixelKSampleUpscalerProvider` - An upscaler is provided that converts latent to pixels using VAEDecode, performs upscaling, converts back to latent using VAEEncode, and then performs k-sampling. This upscaler can be attached to nodes such as `Iterative Upscale` for use.
+  * Similar to `Latent Scale (on Pixel Space)`, if upscale_model_opt is provided, it performs pixel upscaling using the model.
+* `PixelTiledKSampleUpscalerProvider` - It is similar to `PixelKSampleUpscalerProvider`, but it uses `ComfyUI_TiledKSampler` and Tiled VAE Decoder/Encoder to avoid GPU VRAM issues at high resolutions.
   * You need to install the [BlenderNeko/ComfyUI_TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) node extension.
 
-* PK_HOOK
-  * DenoiseScheduleHookProvider - IterativeUpscale provides a hook that gradually changes the denoise to target_denoise as the iterative-step progresses.
-  * CfgScheduleHookProvider - IterativeUpscale provides a hook that gradually changes the cfg to target_cfg as the iterative-step progresses.
-  * StepsScheduleHookProvider - IterativeUpscale provides a hook that gradually changes the sampling-steps to target_steps as the iterative-step progresses.
-  * NoiseInjectionHookProvider - During each iteration of IterativeUpscale, noise is injected into the latent space while varying the strength according to a schedule.
+### PK_HOOK
+  * `DenoiseScheduleHookProvider` - IterativeUpscale provides a hook that gradually changes the denoise to target_denoise as the iterative-step progresses.
+  * `CfgScheduleHookProvider` - IterativeUpscale provides a hook that gradually changes the cfg to target_cfg as the iterative-step progresses.
+  * `StepsScheduleHookProvider` - IterativeUpscale provides a hook that gradually changes the sampling-steps to target_steps as the iterative-step progresses.
+  * `NoiseInjectionHookProvider` - During each iteration of IterativeUpscale, noise is injected into the latent space while varying the strength according to a schedule.
     * You need to install the [BlenderNeko/ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) node extension.
     * The seed serves as the initial value required for generating noise, and it increments by 1 with each iteration as the process unfolds.
     * The source determines the types of CPU noise and GPU noise to be configured.
     * Currently, there is only a simple schedule available, where the strength of the noise varies from start_strength to end_strength during the progression of each iteration.
-  * UnsamplerHookProvider - Apply Unsampler during each iteration. To use this node, ComfyUI_Noise must be installed.
-  * PixelKSampleHookCombine - This is used to connect two PK_HOOKs. hook1 is executed first and then hook2 is executed.
+  * `UnsamplerHookProvider` - Apply Unsampler during each iteration. To use this node, ComfyUI_Noise must be installed.
+  * `PixelKSampleHookCombine` - This is used to connect two PK_HOOKs. hook1 is executed first and then hook2 is executed.
     * If you want to simultaneously change cfg and denoise, you can combine the PK_HOOKs of CfgScheduleHookProvider and PixelKSampleHookCombine.
  
-* DETAILER_HOOK
-  * NoiseInjectionDetailerHookProvider - The `detailer_hook` is a hook in the `Detailer` that injects noise during the processing of each SEGS.
-  * UnsamplerDetailerHookProvider - Apply Unsampler during each cycle. To use this node, ComfyUI_Noise must be installed.
-  * DenoiseSchedulerDetailerHookProvider - During the progress of the cycle, the detailer's denoise is altered up to the `target_denoise`. 
-  * CoreMLDetailerHookProvider - CoreML supports only 512x512, 512x768, 768x512, 768x768 size sampling. CoreMLDetailerHookProvider precisely fixes the upscale of the crop_region to this size. When using this hook, it will always be selected size, regardless of the guide_size. However, if the guide_size is too small, skipping will occur.
-  * DetailerHookCombine - This is used to connect two DETAILER_HOOKs. Similar to PixelKSampleHookCombine.
-  * SEGSOrderedFilterDetailerHook, SEGSRangeFilterDetailerHook, SEGSLabelFilterDetailerHook - There are a wrapper node that provides SEGSFilter nodes to be applied in FaceDetailer or Detector by creating DETAILER_HOOK.
-  * 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.
+### DETAILER_HOOK
+  * `NoiseInjectionDetailerHookProvider` - The `detailer_hook` is a hook in the `Detailer` that injects noise during the processing of each SEGS.
+  * `UnsamplerDetailerHookProvider` - Apply Unsampler during each cycle. To use this node, ComfyUI_Noise must be installed.
+  * `DenoiseSchedulerDetailerHookProvider` - During the progress of the cycle, the detailer's denoise is altered up to the `target_denoise`. 
+  * `CoreMLDetailerHookProvider` - CoreML supports only 512x512, 512x768, 768x512, 768x768 size sampling. CoreMLDetailerHookProvider precisely fixes the upscale of the crop_region to this size. When using this hook, it will always be selected size, regardless of the guide_size. However, if the guide_size is too small, skipping will occur.
+  * `DetailerHookCombine` - This is used to connect two DETAILER_HOOKs. Similar to PixelKSampleHookCombine.
+  * `SEGSOrderedFilterDetailerHook`, SEGSRangeFilterDetailerHook, SEGSLabelFilterDetailerHook - There are a wrapper node that provides SEGSFilter nodes to be applied in FaceDetailer or Detector by creating DETAILER_HOOK.
+  * `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.
 
-* Iterative Upscale (Latent/on Pixel Space) - The upscaler takes the input upscaler and splits the scale_factor into steps, then iteratively performs upscaling. 
-This takes latent as input and outputs latent as the result.
-* Iterative Upscale (Image) - The upscaler takes the input upscaler and splits the scale_factor into steps, then iteratively performs upscaling. This takes image as input and outputs image as the result.
-  * Internally, this node uses 'Iterative Upscale (Latent)'.
+### 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. 
+  This takes latent as input and outputs latent as the result.
+  * `Iterative Upscale (Image)` - The upscaler takes the input upscaler and splits the scale_factor into steps, then iteratively performs upscaling. This takes image as input and outputs image as the result.
+    * Internally, this node uses 'Iterative Upscale (Latent)'.
 
-* TwoSamplersForMask - This node can apply two samplers depending on the mask area. The base_sampler is applied to the area where the mask is 0, while the mask_sampler is applied to the area where the mask is 1.
+### TwoSamplers nodes
+* `TwoSamplersForMask` - This node can apply two samplers depending on the mask area. The base_sampler is applied to the area where the mask is 0, while the mask_sampler is applied to the area where the mask is 1.
   * Note: The latent encoded through VAEEncodeForInpaint cannot be used.
-* KSamplerProvider - This is a wrapper that enables KSampler to be used in TwoSamplersForMask TwoSamplersForMaskUpscalerProvider.
-* TiledKSamplerProvider - ComfyUI_TiledKSampler is a wrapper that provides KSAMPLER.
+* `KSamplerProvider` - This is a wrapper that enables KSampler to be used in TwoSamplersForMask TwoSamplersForMaskUpscalerProvider.
+* `TiledKSamplerProvider` - ComfyUI_TiledKSampler is a wrapper that provides KSAMPLER.
   * You need to install the [BlenderNeko/ComfyUI_TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) node extension.
   
-* TwoAdvancedSamplersForMask - TwoSamplersForMask is similar to TwoAdvancedSamplersForMask, but they differ in their operation. TwoSamplersForMask performs sampling in the mask area only after all the samples in the base area are finished. On the other hand, TwoAdvancedSamplersForMask performs sampling in both the base area and the mask area sequentially at each step.
-* KSamplerAdvancedProvider - This is a wrapper that enables KSampler to be used in TwoAdvancedSamplersForMask, RegionalSampler.
+* `TwoAdvancedSamplersForMask` - TwoSamplersForMask is similar to TwoAdvancedSamplersForMask, but they differ in their operation. TwoSamplersForMask performs sampling in the mask area only after all the samples in the base area are finished. On the other hand, TwoAdvancedSamplersForMask performs sampling in both the base area and the mask area sequentially at each step.
+* `KSamplerAdvancedProvider` - This is a wrapper that enables KSampler to be used in TwoAdvancedSamplersForMask, RegionalSampler.
   * sigma_factor: By multiplying the denoise schedule by the sigma_factor, you can adjust the amount of denoising based on the configured denoise.
 
-* TwoSamplersForMaskUpscalerProvider - This is an Upscaler that extends TwoSamplersForMask to be used in Iterative Upscale.
+* `TwoSamplersForMaskUpscalerProvider` - This is an Upscaler that extends TwoSamplersForMask to be used in Iterative Upscale.
   * TwoSamplersForMaskUpscalerProviderPipe - pipe version of TwoSamplersForMaskUpscalerProvider.
 
-* Image Utils
-  * PreviewBridge (image) - This custom node can be used with a bridge for image when using the MaskEditor feature of Clipspace.
-  * PreviewBridge (latent) - This custom node can be used with a bridge for latent image when using the MaskEditor feature of Clipspace.
+### Image Utils
+  * `PreviewBridge (image)` - This custom node can be used with a bridge for image when using the MaskEditor feature of Clipspace.
+  * `PreviewBridge (latent)` - This custom node can be used with a bridge for latent image when using the MaskEditor feature of Clipspace.
     * If a latent with a mask is provided as input, it displays the mask. Additionally, the mask output provides the mask set in the latent.
     * If a latent without a mask is provided as input, it outputs the original latent as is, but the mask output provides an output with the entire region set as a mask.
     * When set mask through MaskEditor, a mask is applied to the latent, and the output includes the stored mask. The same mask is also output as the mask output.
     * When connected to `vae_opt`, it takes higher priority than the `preview_method`.
-  * ImageSender, ImageReceiver - The images generated in ImageSender are automatically sent to the ImageReceiver with the same link_id.
-  * LatentSender, LatentReceiver - The latent generated in LatentSender are automatically sent to the LatentReceiver with the same link_id.
+  * `ImageSender`, `ImageReceiver` - The images generated in ImageSender are automatically sent to the ImageReceiver with the same link_id.
+  * `LatentSender`, `LatentReceiver` - The latent generated in LatentSender are automatically sent to the LatentReceiver with the same link_id.
     * Furthermore, LatentSender is implemented with PreviewLatent, which stores the latent in payload form within the image thumbnail.
     * Due to the current structure of ComfyUI, it is unable to distinguish between SDXL latent and SD1.5/SD2.1 latent. Therefore, it generates thumbnails by decoding them using the SD1.5 method.
 
-* Switch nodes
-  * Switch (image,mask), Switch (latent), Switch (SEGS) - Among multiple inputs, it selects the input designated by the selector and outputs it. The first input must be provided, while the others are optional. However, if the input specified by the selector is not connected, an error may occur.
-  * Switch (Any) - This is a Switch node that takes an arbitrary number of inputs and produces a single output. Its type is determined when connected to any node, and connecting inputs increases the available slots for connections.
-  * Inversed Switch (Any) - In contrast to `Switch (Any)`, it takes a single input and outputs one of many. Due to ComfyUI's functional limitations, the value of `select` must be determined at the time of queuing a prompt, and while it can serve as a `Primitive Node` or `ImpactInt`, it cannot function properly when connected through other nodes. 
-  * Guide
-    * When the `Switch (Any)` and `Inversed Switch (Any)` selects are transformed into primitives, it's important to be cautious because the select range is not appropriately constrained, potentially leading to unintended behavior.   
-    * `Switch (image,mask)`, `Switch (latent)`, `Switch (SEGS)`, `Switch (Any)` supports `sel_mode` param. The `sel_mode` sets the moment at which the `select` parameter is determined. `select_on_prompt` determines the `select` at the time of queuing the prompt, while `select_on_execution` determines it during the execution of the workflow. While `select_on_execution` offers more flexibility, it can potentially trigger workflow execution errors due to running nodes that may be impossible to execute within the limitations of ComfyUI. `select_on_prompt` bypasses this constraint by treating any inputs not selected as if they were disconnected. However, please note that when using `select_on_prompt`, the `select` can only be used with widgets or `Primitive Nodes` determined at the queue prompt.
-    * There is an issue when connecting the built-in reroute node with the switch's input/output slots. it can lead to forced disconnections during workflow loading. Therefore, it is advisable not to use reroute for making connections in such cases. However, there are no issues when using the reroute node in Pythongossss.
+### Switch nodes
+  * `Switch (image,mask)`, `Switch (latent)`, `Switch (SEGS)` - Among multiple inputs, it selects the input designated by the selector and outputs it. The first input must be provided, while the others are optional. However, if the input specified by the selector is not connected, an error may occur.
+  * `Switch (Any)` - This is a Switch node that takes an arbitrary number of inputs and produces a single output. Its type is determined when connected to any node, and connecting inputs increases the available slots for connections.
+  * `Inversed Switch (Any)` - In contrast to `Switch (Any)`, it takes a single input and outputs one of many.
+  * NOTE: See this [tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/switch.md) 
 
-* [Wildcards](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md) - These are nodes that supports syntax in the form of `__wildcard-name__` and dynamic prompt syntax like `{a|b|c}`.
+### [Wildcards](http://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md) nodes
+  * These are nodes that supports syntax in the form of `__wildcard-name__` and dynamic prompt syntax like `{a|b|c}`.
   * Wildcard files can be used by placing `.txt` or `.yaml` files under either `ComfyUI-Impact-Pack/wildcards` or `ComfyUI-Impact-Pack/custom_wildcards` paths.
     * You can download and use [Wildcard YAML](https://civitai.com/models/138970/billions-of-wildcards-all-in-one) files in this format.
     * After the first execution, you can change the custom wildcards path in the `custom_wildcards` entry within the `ComfyUI-Impact-Pack/impact-pack.ini` file created.
-  * ImpactWildcardProcessor - The text is generated by processing the wildcard in the Text. If the mode is set to "populate", a dynamic prompt is generated with each execution and the input is filled in the second textbox. If the mode is set to "fixed", the content of the second textbox remains unchanged.
+  * `ImpactWildcardProcessor` - The text is generated by processing the wildcard in the Text. If the mode is set to "populate", a dynamic prompt is generated with each execution and the input is filled in the second textbox. If the mode is set to "fixed", the content of the second textbox remains unchanged.
     * When an image is generated with the "fixed" mode, the prompt used for that particular generation is stored in the metadata.
-  * ImpactWildcardEncode - Similar to ImpactWildcardProcessor, this provides the loading functionality of LoRAs (e.g. `<lora:some_awesome_lora:0.7:1.2>`). Populated prompts are encoded using the clip after all the lora loading is done.
+  * `ImpactWildcardEncode` - Similar to ImpactWildcardProcessor, this provides the loading functionality of LoRAs (e.g. `<lora:some_awesome_lora:0.7:1.2>`). Populated prompts are encoded using the clip after all the lora loading is done.
     * If the `Inspire Pack` is installed, you can use **Lora Block Weight** in the form of `LBW=lbw spec;`
     * `<lora:chunli:1.0:1.0:LBW=B11:0,0,0,0,0,0,0,0,0,0,A,0,0,0,0,0,0;A=0.;>`, `<lora:chunli:1.0:1.0:LBW=0,0,0,0,0,0,0,0,0,0,A,B,0,0,0,0,0;A=0.5;B=0.2;>`, `<lora:chunli:1.0:1.0:LBW=SD-MIDD;>`
 
-* Regional Sampling - These nodes offer the capability to divide regions and perform partial sampling using a mask. Unlike TwoSamplersForMask, sampling for each region is applied during each step.
-  * RegionalPrompt - This node combines a **mask** for specifying regions and the **sampler** to apply to each region to create `REGIONAL_PROMPTS`.
-  * CombineRegionalPrompts - Combine multiple `REGIONAL_PROMPTS` to create a single `REGIONAL_PROMPTS`.
-  * RegionalSampler - This node performs sampling using a base sampler and regional prompts. Sampling by the base sampler is executed at each step, while sampling for each region is performed through the sampler bound to each region.
+### Regional Sampling
+  * These nodes offer the capability to divide regions and perform partial sampling using a mask. Unlike TwoSamplersForMask, sampling for each region is applied during each step.
+  * `RegionalPrompt` - This node combines a **mask** for specifying regions and the **sampler** to apply to each region to create `REGIONAL_PROMPTS`.
+  * `CombineRegionalPrompts` - Combine multiple `REGIONAL_PROMPTS` to create a single `REGIONAL_PROMPTS`.
+  * `RegionalSampler` - This node performs sampling using a base sampler and regional prompts. Sampling by the base sampler is executed at each step, while sampling for each region is performed through the sampler bound to each region.
     * overlap_factor - Specifies the amount of overlap for each region to blend well with the area outside the mask.
     * restore_latent - When sampling each region, restore the areas outside the mask to the base latent, preventing additional noise from being introduced outside the mask during region sampling.
-  * RegionalSamplerAdvanced - This is the Advanced version of the RegionalSampler. You can control it using `step` instead of `denoise`.
-  * NOTE: The `sde` sampler and `uni_pc` sampler introduce additional noise during each step of the sampling process. To mitigate this, when sampling each region, the `uni_pc` sampler applies additional `dpmpp_fast`, and the sde sampler applies the `dpmpp_2m` sampler as an additional measure.
+  * `RegionalSamplerAdvanced` - This is the Advanced version of the RegionalSampler. You can control it using `step` instead of `denoise`.
+    > NOTE: The `sde` sampler and `uni_pc` sampler introduce additional noise during each step of the sampling process. To mitigate this, when sampling each region, the `uni_pc` sampler applies additional `dpmpp_fast`, and the sde sampler applies the `dpmpp_2m` sampler as an additional measure.
 
-* KSampler (pipe), KSampler (advanced/pipe)
 
-* Image batch To Image List - Convert Image batch to Image List
-- You can use images generated in a multi batch to handle them
-* Make Image List - Convert multiple images into a single image list
-* Make Image Batch - Convert multiple images into a single image batch
-- The input of images can be scaled up as needed
+### Impact KSampler
+  * These samplers support basic_pipe and AYS 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.
+  * `GITSScheduler Func Provider` - provider scheduler function for GITSScheduler
+  
 
-* String Selector - It selects and returns a portion of the string. When `multiline` mode is disabled, it simply returns the string of the line pointed to by the selector. When `multiline` mode is enabled, it divides the string based on lines that start with `#` and returns them. If the `select` value is larger than the number of items, it will start counting from the first line again and return accordingly.
-* Combine Conditionings - It takes multiple conditionings as input and combines them into a single conditioning.
-* Concat Conditionings - It takes multiple conditionings as input and concat them into a single conditioning.
+### Batch/List Util
+  * `Image Batch to Image List` - Convert Image batch to Image List
+    - You can use images generated in a multi batch to handle them
+  * `Image List to Image Batch` - Convert Image List to Image Batch 
+  * `Make Image List` - Convert multiple images into a single image list
+  * `Make Image Batch` - Convert multiple images into a single image batch
+    - The input of images can be scaled up as needed
+  * `Masks to Mask List`, `Mask List to Masks`, `Make Mask List`, `Make Mask Batch` - It has the same functionality as the nodes above, but uses mask as input instead of image.
+  * `Flatten Mask Batch` - Flattens a Mask Batch into a single Mask. Normal operation is not guaranteed for non-binary masks. 
+  * `Make List (Any)` - Create a list with arbitrary values.
 
-* Logics (experimental) - These nodes are experimental nodes designed to implement the logic for loops and dynamic switching.
-  * ImpactCompare, ImpactConditionalBranch, ImpactConditionalBranchSelMode, ImpactInt, ImpactValueSender, ImpactValueReceiver, ImpactImageInfo, ImpactMinMax, ImpactNeg, ImpactConditionalStopIteration
-  * ImpactIsNotEmptySEGS - This node returns `true` only if the input SEGS is not empty. 
-  * Queue Trigger - When this node is executed, it adds a new queue to assist with repetitive tasks. It will only execute if the signal's status changes.
-  * Queue Trigger (Countdown) - Like the Queue Trigger, it adds a queue, but only adds it if it's greater than 1, and decrements the count by one each time it runs.
-  * Sleep - Waits for the specified time (in seconds).
-  * Set Widget Value - This node sets one of the optional inputs to the specified node's widget. An error may occur if the types do not match.
-  * Set Mute State - This node changes the mute state of a specific node.
-  * Control Bridge - This node modifies the state of the connected control nodes based on the `mode` and `behavior` . If there are nodes that require a change, the current execution is paused, the mute status is updated, and a new prompt queue is inserted. 
+### Logics (experimental) 
+  * These nodes are experimental nodes designed to implement the logic for loops and dynamic switching.
+  * `ImpactCompare`, `ImpactConditionalBranch`, `ImpactConditionalBranchSelMode`, `ImpactInt`, `ImpactBoolean`, `ImpactValueSender`, `ImpactValueReceiver`, `ImpactImageInfo`, `ImpactMinMax`, `ImpactNeg`, `ImpactConditionalStopIteration`
+  * `ImpactIsNotEmptySEGS` - This node returns `true` only if the input SEGS is not empty. 
+  * `ImpactIfNone` - Returns `true` if any_input is None, and returns `false` if it is not None.
+  * `Queue Trigger` - When this node is executed, it adds a new queue to assist with repetitive tasks. It will only execute if the signal's status changes.
+  * `Queue Trigger (Countdown)` - Like the Queue Trigger, it adds a queue, but only adds it if it's greater than 1, and decrements the count by one each time it runs.
+  * `Sleep` - Waits for the specified time (in seconds).
+  * `Set Widget Value` - This node sets one of the optional inputs to the specified node's widget. An error may occur if the types do not match.
+  * `Set Mute State` - This node changes the mute state of a specific node.
+  * `Control Bridge` - This node modifies the state of the connected control nodes based on the `mode` and `behavior` . If there are nodes that require a change, the current execution is paused, the mute status is updated, and a new prompt queue is inserted. 
     * When the `mode` is `active`, it makes the connected control nodes active regardless of the behavior. 
     * When the `mode` is `Bypass/Mute`, it changes the state of the connected nodes based on whether the behavior is `Bypass` or `Mute`.
     * **Limitation**: Due to these characteristics, it does not function correctly when the batch count exceeds 1. Additionally, it does not guarantee proper operation when the seed is randomized or when the state of nodes is altered by actions such as `Queue Trigger`, `Set Widget Value`, `Set Mute`, before the Control Bridge.
     * When utilizing this node, please structure the workflow in such a way that `Queue Trigger`, `Set Widget Value`, `Set Mute State`, and similar actions are executed at the end of the workflow.
     * If you want to change the value of the seed at each iteration, please ensure that Set Widget Value is executed at the end of the workflow instead of using randomization.
       * It is not a problem if the seed changes due to randomization as long as it occurs after the Control Bridge section.
-  * Remote Boolean (on prompt), Remote Int (on prompt) - At the start of the prompt, this node forcibly sets the `widget_value` of `node_id`. It is disregarded if the target widget type is different.
+  * `Remote Boolean (on prompt)`, `Remote Int (on prompt)` - At the start of the prompt, this node forcibly sets the `widget_value` of `node_id`. It is disregarded if the target widget type is different.
   * You can find the `node_id` by checking through [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) using the format `Badge: #ID Nickname`.
   * Experimental set of nodes for implementing loop functionality (tutorial to be prepared later / [example workflow](test/loop-test.json)).
 
-* HuggingFace - These nodes provide functionalities based on HuggingFace repository models.
+### HuggingFace nodes
+  * These nodes provide functionalities based on HuggingFace repository models.
+  * The path where the HuggingFace model cache is stored can be changed through the `HF_HOME` environment variable.
   * `HF Transformers Classifier Provider` - This is a node that provides a classifier based on HuggingFace's transformers models.
     * The 'repo id' parameter should contain HuggingFace's repo id. When `preset_repo_id` is set to `Manual repo id`, use the manually entered repo id in `manual_repo_id`.
     * e.g. 'rizvandwiki/gender-classification-2' is a repository that provides a model for gender classification.
@@ -248,80 +275,62 @@ This takes latent as input and outputs latent as the result.
       * For supported labels, please refer to the `config.json` of the respective HuggingFace repository.
     * `#Female` and `#Male` are symbols that group multiple labels such as `Female, women, woman, ...`, for convenience, rather than being single labels.
 
-## MMDet nodes
-* MMDetDetectorProvider - Loads the MMDet model to provide BBOX_DETECTOR and SEGM_DETECTOR.
-* To use the existing MMDetDetectorProvider, you need to enable the MMDet usage configuration.
+
+### Etc nodes
+  * `Impact Scheduler Adapter` - With the addition of AYS to the scheduler of the Impact Pack and Inspire Pack, there is an issue of incompatibility when the existing scheduler widget is converted to input. The Impact Scheduler Adapter allows for an indirect connection to be possible.
+  * `StringListToString` - Convert String List to String
+  * `WildcardPromptFromString` - Create labeled wildcard for detailer from string. 
+    * This node works well when used with MakeTileSEGS. [[Link](https://github.com/ltdrdata/ComfyUI-Impact-Pack/pull/536#discussion_r1586060779)]
+
+  * `String Selector` - It selects and returns a portion of the string. When `multiline` mode is disabled, it simply returns the string of the line pointed to by the selector. When `multiline` mode is enabled, it divides the string based on lines that start with `#` and returns them. If the `select` value is larger than the number of items, it will start counting from the first line again and return accordingly.
+  * `Combine Conditionings` - It takes multiple conditionings as input and combines them into a single conditioning.
+  * `Concat Conditionings` - It takes multiple conditionings as input and concat them into a single conditioning.
+  * `Negative Cond Placeholder` - Models like FLUX.1 do not use Negative Conditioning. This is a placeholder node for them. You can use FLUX.1 by replacing the Negative Conditioning used in Impact KSampler, KSampler (Inspire), and Detailer with this node.
+  * `Execution Order Controller` - A helper node that can forcibly control the execution order of nodes.
+    * Connect the output of the node that should be executed first to the signal, and make the input of the node that should be executed later pass through this node.
+  * `List Bridge` - When passing the list output through this node, it collects and organizes the data before forwarding it, which ensures that the previous stage's sub-workflow has been completed.
 
 
 ## Feature
-* Interactive SAM Detector (Clipspace) - When you right-click on a node that has 'MASK' and 'IMAGE' outputs, a context menu will open. From this menu, you can either open a dialog to create a SAM Mask using 'Open in SAM Detector', or copy the content (likely mask data) using 'Copy (Clipspace)' and generate a mask using 'Impact SAM Detector' from the clipspace menu, and then paste it using 'Paste (Clipspace)'.
+* `Interactive SAM Detector (Clipspace)` - When you right-click on a node that has 'MASK' and 'IMAGE' outputs, a context menu will open. From this menu, you can either open a dialog to create a SAM Mask using 'Open in SAM Detector', or copy the content (likely mask data) using 'Copy (Clipspace)' and generate a mask using 'Impact SAM Detector' from the clipspace menu, and then paste it using 'Paste (Clipspace)'.
 * Providing a feature to detect errors that occur when mixing models and clips from checkpoints such as `SDXL Base`, `SDXL Refiner`, `SD1.x`, `SD2.x` during sample execution, and reporting appropriate errors.
 
-## Deprecated
-* The following nodes have been kept only for compatibility with existing workflows, and are no longer supported. Please replace them with new nodes.
-   * ONNX Detector (SEGS) - BBOX Detector (SEGS)
-   * MMDetLoader -> MMDetDetectorProvider
-   * SegsMaskCombine -> SEGS to MASK (combined)
-   * BboxDetectorForEach -> BBOX Detector (SEGS)
-   * SegmDetectorForEach -> SEGM Detector (SEGS)
-   * BboxDetectorCombined -> BBOX Detector (combined)
-   * SegmDetectorCombined -> SEGM Detector (combined)
-   * MaskPainter -> PreviewBridge
-* To use the existing deprecated legacy nodes, you need to enable the MMDet usage configuration.
 
+## How To Install?
 
-## Ultralytics models
-* huggingface.co/Bingsu/[adetailer](https://github.com/ultralytics/assets/releases/) - You can download face, people detection models, and clothing detection models.
-* ultralytics/[assets](https://github.com/ultralytics/assets/releases/) - You can download various types of detection models other than faces or people.
-* civitai/[adetailer](https://civitai.com/search/models?sortBy=models_v5&query=adetailer) - You can download various types detection models....Many models are associated with NSFW content.
-
-## How to activate 'MMDet usage'
-* Upon the initial execution, an `impact-pack.ini` file will be generated in the custom_nodes/ComfyUI-Impact-Pack directory.
-```
-[default]
-dependency_version = 2
-mmdet_skip = True
-```
-* Change `mmdet_skip = True` to `mmdet_skip = False`
-```
-[default]
-dependency_version = 2
-mmdet_skip = False
-```
-* Restart ComfyUI
-
-
-## Installation
+### Install via ComfyUI-Manager (Recommended)
+* Search `ComfyUI Impact Pack` in ComfyUI-Manager and click `Install` button.
 
+### Manual Install (Not Recommended)
 1. `cd custom_nodes`
-1. `git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack.git`
+2. `git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack`
 3. `cd ComfyUI-Impact-Pack`
-4. (optional) `git submodule update --init --recursive`
-   * Impact Pack will automatically download subpack during its initial launch.
-5. (optional) `python install.py`
-   * Impact Pack will automatically install its dependencies during its initial launch.
-   * For the portable version, you should execute the command `..\..\..\python_embeded\python.exe install.py` to run the installation script.
+4. `pip install -r requirements.txt`
+    * **IMPORTANT**:
+        * You must install it within the Python environment where ComfyUI is running.
+        * For the portable version, use `<installed path>\python_embeded\python.exe -m pip` instead of `pip`. For a `venv`, activate the `venv` first and then use `pip`.
+5. Restart ComfyUI
 
+* NOTE1: If an error occurs during the installation process, please refer to [Troubleshooting Page](troubleshooting/TROUBLESHOOTING.md) for assistance. 
+* NOTE2: You can use this colab notebook [colab notebook](https://colab.research.google.com/github/ltdrdata/ComfyUI-Impact-Pack/blob/Main/notebook/comfyui_colab_impact_pack.ipynb) to launch it. This notebook automatically downloads the impact pack to the custom_nodes directory, installs the tested dependencies, and runs it.
+* NOTE3: If you create an empty file named `skip_download_model` in the `ComfyUI/custom_nodes/` directory, it will skip the model download step during the installation of the impact pack.
 
-6. Restart ComfyUI
-
-* NOTE: If an error occurs during the installation process, please refer to [Troubleshooting Page](troubleshooting/TROUBLESHOOTING.md) for assistance. 
-* You can use this colab notebook [colab notebook](https://colab.research.google.com/github/ltdrdata/ComfyUI-Impact-Pack/blob/Main/notebook/comfyui_colab_impact_pack.ipynb) to launch it. This notebook automatically downloads the impact pack to the custom_nodes directory, installs the tested dependencies, and runs it.
 
 ## Package Dependencies (If you need to manual setup.)
 
 * pip install
-   * openmim
    * segment-anything
-   * ultralytics
    * scikit-image
-   * piexif
-   * (optional) pycocotools
+   * piexif 
+   * opencv-python
+   * scipy
+   * numpy<2
+   * dill
+   * matplotlib
    * (optional) onnxruntime
+   * (deprecated) openmim      # for mim
+   * (deprecated) pycocotools  # for mim
    
-* mim install (optional)
-   * mmcv==2.0.0, mmdet==3.0.0, mmengine==0.7.2
-
 * linux packages (ubuntu)
   * libgl1-mesa-glx
   * libglib2.0-0
@@ -344,17 +353,16 @@ sam_editor_model = sam_vit_b_01ec64.pth
 ```
 
 
-## Other Materials (auto-download on initial startup)
+## Other Materials (auto-download when installing)
 
-* ComfyUI/models/mmdets/bbox <= https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/bbox/mmdet_anime-face_yolov3.pth
-* ComfyUI/models/mmdets/bbox <= https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/mmdet_anime-face_yolov3.py
 * ComfyUI/models/sams <= https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth
 
+
 ## Troubleshooting page
 * [Troubleshooting Page](troubleshooting/TROUBLESHOOTING.md)
 
 
-## How to use (DDetailer feature)
+## How To Use (DDetailer feature)
 
 #### 1. Basic auto face detection and refine exapmle.
 ![simple](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple.png)
@@ -445,10 +453,9 @@ open-mmlab/[mmdetection](https://github.com/open-mmlab/mmdetection) - Object det
 
 biegert/[ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg) - This is a custom node that enables the use of CLIPSeg technology, which can find segments through prompts, in ComfyUI.
 
-BlenderNeok/[ComfyUI-TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) - 
-The tile sampler allows high-resolution sampling even in places with low GPU VRAM.
+BlenderNeok/[ComfyUI-TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) - The tile sampler allows high-resolution sampling even in places with low GPU VRAM.
 
-BlenderNeok/[ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) - The noise injection feature relies on this function.
+BlenderNeok/[ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) - The noise injection feature relies on this function and slerp code for noise variation
 
 WASasquatch/[was-node-suite-comfyui](https://github.com/WASasquatch/was-node-suite-comfyui) - A powerful custom node extensions of ComfyUI.
 

__init__.py

@@ -13,10 +13,7 @@ import traceback
 
 comfy_path = os.path.dirname(folder_paths.__file__)
 impact_path = os.path.join(os.path.dirname(__file__))
-subpack_path = os.path.join(os.path.dirname(__file__), "impact_subpack")
 modules_path = os.path.join(os.path.dirname(__file__), "modules")
-wildcards_path = os.path.join(os.path.dirname(__file__), "wildcards")
-custom_wildcards_path = os.path.join(os.path.dirname(__file__), "custom_wildcards")
 
 sys.path.append(modules_path)
 
@@ -24,33 +21,13 @@ import impact.config
 import impact.sample_error_enhancer
 print(f"### Loading: ComfyUI-Impact-Pack ({impact.config.version})")
 
-
-def do_install():
-    import importlib
-    spec = importlib.util.spec_from_file_location('impact_install', os.path.join(os.path.dirname(__file__), 'install.py'))
-    impact_install = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(impact_install)
-
-
-# ensure dependency
-if not os.path.exists(os.path.join(subpack_path, ".git")) and os.path.exists(subpack_path):
-    print(f"### CompfyUI-Impact-Pack: corrupted subpack detected.")
-    shutil.rmtree(subpack_path)
-
-if impact.config.get_config()['dependency_version'] < impact.config.dependency_version or not os.path.exists(subpack_path):
-    print(f"### ComfyUI-Impact-Pack: Updating dependencies [{impact.config.get_config()['dependency_version']} -> {impact.config.dependency_version}]")
-    do_install()
-
-sys.path.append(subpack_path)
-
 # Core
 # recheck dependencies for colab
 try:
-    import impact.subpack_nodes  # This import must be done before cv2.
-
     import folder_paths
     import torch
     import cv2
+    from cv2 import setNumThreads
     import numpy as np
     import comfy.samplers
     import comfy.sd
@@ -64,68 +41,31 @@ try:
         import mmcv
         from mmdet.apis import (inference_detector, init_detector)
         from mmdet.evaluation import get_classes
-except:
-    import importlib
-    print("### ComfyUI-Impact-Pack: Reinstall dependencies (several dependencies are missing.)")
-    do_install()
+except Exception as e:
+    import logging
+    logging.error("[Impact Pack] Failed to import due to several dependencies are missing!!!!")
+    raise e
+
 
 import impact.impact_server  # to load server api
 
-def setup_js():
-    import nodes
-    js_dest_path = os.path.join(comfy_path, "web", "extensions", "impact-pack")
-
-    if hasattr(nodes, "EXTENSION_WEB_DIRS"):
-        if os.path.exists(js_dest_path):
-            shutil.rmtree(js_dest_path)
-    else:
-        print(f"[WARN] ComfyUI-Impact-Pack: Your ComfyUI version is outdated. Please update to the latest version.")
-        # setup js
-        if not os.path.exists(js_dest_path):
-            os.makedirs(js_dest_path)
-
-        js_src_path = os.path.join(impact_path, "js", "impact-pack.js")
-        shutil.copy(js_src_path, js_dest_path)
-
-        js_src_path = os.path.join(impact_path, "js", "impact-sam-editor.js")
-        shutil.copy(js_src_path, js_dest_path)
-
-        js_src_path = os.path.join(impact_path, "js", "comboBoolMigration.js")
-        shutil.copy(js_src_path, js_dest_path)
-
-
-setup_js()
-
-from impact.impact_pack import *
-from impact.detectors import *
-from impact.pipe import *
-from impact.logics import *
-from impact.util_nodes import *
-from impact.segs_nodes import *
-from impact.special_samplers import *
-from impact.hf_nodes import *
-from impact.bridge_nodes import *
-from impact.hook_nodes import *
-from impact.animatediff_nodes import *
+from .modules.impact.impact_pack import *
+from .modules.impact.detectors import *
+from .modules.impact.pipe import *
+from .modules.impact.logics import *
+from .modules.impact.util_nodes import *
+from .modules.impact.segs_nodes import *
+from .modules.impact.special_samplers import *
+from .modules.impact.hf_nodes import *
+from .modules.impact.bridge_nodes import *
+from .modules.impact.hook_nodes import *
+from .modules.impact.animatediff_nodes import *
+from .modules.impact.segs_upscaler import *
 
 import threading
 
-wildcard_path = impact.config.get_config()['custom_wildcards']
 
-
-def wildcard_load():
-    with wildcards.wildcard_lock:
-        impact.wildcards.read_wildcard_dict(wildcards_path)
-
-        try:
-            impact.wildcards.read_wildcard_dict(impact.config.get_config()['custom_wildcards'])
-        except Exception as e:
-            print(f"[Impact Pack] Failed to load custom wildcards directory.")
-
-        print(f"[Impact Pack] Wildcards loading done.")
-
-
-threading.Thread(target=wildcard_load).start()
+threading.Thread(target=impact.wildcards.wildcard_load).start()
 
 
 NODE_CLASS_MAPPINGS = {
@@ -154,6 +94,7 @@ NODE_CLASS_MAPPINGS = {
     "FromDetailerPipe": FromDetailerPipe,
     "FromDetailerPipe_v2": FromDetailerPipe_v2,
     "FromDetailerPipeSDXL": FromDetailerPipe_SDXL,
+    "AnyPipeToBasic": AnyPipeToBasic,
     "ToBasicPipe": ToBasicPipe,
     "FromBasicPipe": FromBasicPipe,
     "FromBasicPipe_v2": FromBasicPipe_v2,
@@ -182,7 +123,6 @@ NODE_CLASS_MAPPINGS = {
     "UnsamplerHookProvider": UnsamplerHookProvider,
     "CoreMLDetailerHookProvider": CoreMLDetailerHookProvider,
     "PreviewDetailerHookProvider": PreviewDetailerHookProvider,
-    "StableCascade_DetailerHookProvider": StableCascade_DetailerHookProvider,
 
     "DetailerHookCombine": DetailerHookCombine,
     "NoiseInjectionDetailerHookProvider": NoiseInjectionDetailerHookProvider,
@@ -191,13 +131,18 @@ NODE_CLASS_MAPPINGS = {
     "SEGSOrderedFilterDetailerHookProvider": SEGSOrderedFilterDetailerHookProvider,
     "SEGSRangeFilterDetailerHookProvider": SEGSRangeFilterDetailerHookProvider,
     "SEGSLabelFilterDetailerHookProvider": SEGSLabelFilterDetailerHookProvider,
+    "VariationNoiseDetailerHookProvider": VariationNoiseDetailerHookProvider,
+    # "CustomNoiseDetailerHookProvider": CustomNoiseDetailerHookProvider,
 
     "BitwiseAndMask": BitwiseAndMask,
     "SubtractMask": SubtractMask,
     "AddMask": AddMask,
+    "MaskRectArea": MaskRectArea,
+    "MaskRectAreaAdvanced": MaskRectAreaAdvanced,
     "ImpactSegsAndMask": SegsBitwiseAndMask,
     "ImpactSegsAndMaskForEach": SegsBitwiseAndMaskForEach,
     "EmptySegs": EmptySEGS,
+    "ImpactFlattenMask": FlattenMask,
 
     "MediaPipeFaceMeshToSEGS": MediaPipeFaceMeshToSEGS,
     "MaskToSEGS": MaskToSEGS,
@@ -230,6 +175,9 @@ NODE_CLASS_MAPPINGS = {
     "ImpactDilateMaskInSEGS": DilateMaskInSEGS,
     "ImpactGaussianBlurMaskInSEGS": GaussianBlurMaskInSEGS,
     "ImpactScaleBy_BBOX_SEG_ELT": SEG_ELT_BBOX_ScaleBy,
+    "ImpactFrom_SEG_ELT_bbox": From_SEG_ELT_bbox,
+    "ImpactFrom_SEG_ELT_crop_region": From_SEG_ELT_crop_region,
+    "ImpactCount_Elts_in_SEGS": Count_Elts_in_SEGS,
 
     "BboxDetectorCombined_v2": BboxDetectorCombined,
     "SegmDetectorCombined_v2": SegmDetectorCombined,
@@ -242,6 +190,8 @@ NODE_CLASS_MAPPINGS = {
     "KSamplerAdvancedProvider": KSamplerAdvancedProvider,
     "TwoAdvancedSamplersForMask": TwoAdvancedSamplersForMask,
 
+    "ImpactNegativeConditioningPlaceholder": NegativeConditioningPlaceholder,
+
     "PreviewBridge": PreviewBridge,
     "PreviewBridgeLatent": PreviewBridgeLatent,
     "ImageSender": ImageSender,
@@ -257,6 +207,8 @@ NODE_CLASS_MAPPINGS = {
     "ImpactWildcardProcessor": ImpactWildcardProcessor,
     "ImpactWildcardEncode": ImpactWildcardEncode,
 
+    "SEGSUpscaler": SEGSUpscaler,
+    "SEGSUpscalerPipe": SEGSUpscalerPipe,
     "SEGSDetailer": SEGSDetailer,
     "SEGSPaste": SEGSPaste,
     "SEGSPreview": SEGSPreview,
@@ -267,6 +219,7 @@ NODE_CLASS_MAPPINGS = {
     "ImpactSEGSConcat": SEGSConcat,
     "ImpactSEGSPicker": SEGSPicker,
     "ImpactMakeTileSEGS": MakeTileSEGS,
+    "ImpactSEGSMerge": SEGSMerge,
 
     "SEGSDetailerForAnimateDiff": SEGSDetailerForAnimateDiff,
 
@@ -279,6 +232,9 @@ NODE_CLASS_MAPPINGS = {
     "ImpactImageBatchToImageList": ImageBatchToImageList,
     "ImpactMakeImageList": MakeImageList,
     "ImpactMakeImageBatch": MakeImageBatch,
+    "ImpactMakeAnyList": MakeAnyList,
+    "ImpactMakeMaskList": MakeMaskList,
+    "ImpactMakeMaskBatch": MakeMaskBatch,
 
     "RegionalSampler": RegionalSampler,
     "RegionalSamplerAdvanced": RegionalSamplerAdvanced,
@@ -301,6 +257,7 @@ NODE_CLASS_MAPPINGS = {
     "ImpactLogicalOperators": ImpactLogicalOperators,
     "ImpactInt": ImpactInt,
     "ImpactFloat": ImpactFloat,
+    "ImpactBoolean": ImpactBoolean,
     "ImpactValueSender": ImpactValueSender,
     "ImpactValueReceiver": ImpactValueReceiver,
     "ImpactImageInfo": ImpactImageInfo,
@@ -309,6 +266,10 @@ NODE_CLASS_MAPPINGS = {
     "ImpactNeg": ImpactNeg,
     "ImpactConditionalStopIteration": ImpactConditionalStopIteration,
     "ImpactStringSelector": StringSelector,
+    "StringListToString": StringListToString,
+    "WildcardPromptFromString": WildcardPromptFromString,
+    "ImpactExecutionOrderController": ImpactExecutionOrderController,
+    "ImpactListBridge": ImpactListBridge,
 
     "RemoveNoiseMask": RemoveNoiseMask,
 
@@ -326,7 +287,10 @@ NODE_CLASS_MAPPINGS = {
     "ImpactRemoteInt": ImpactRemoteInt,
 
     "ImpactHFTransformersClassifierProvider": HF_TransformersClassifierProvider,
-    "ImpactSEGSClassify": SEGS_Classify
+    "ImpactSEGSClassify": SEGS_Classify,
+
+    "ImpactSchedulerAdapter": ImpactSchedulerAdapter,
+    "GITSSchedulerFuncProvider": GITSSchedulerFuncProvider
 }
 
 
@@ -339,8 +303,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImpactSimpleDetectorSEGS_for_AD": "Simple Detector for AnimateDiff (SEGS)",
     "ImpactSimpleDetectorSEGS": "Simple Detector (SEGS)",
     "ImpactSimpleDetectorSEGSPipe": "Simple Detector (SEGS/pipe)",
-    "ImpactControlNetApplySEGS": "ControlNetApply (SEGS)",
-    "ImpactControlNetApplyAdvancedSEGS": "ControlNetApplyAdvanced (SEGS)",
+    "ImpactControlNetApplySEGS": "ControlNetApply (SEGS) - DEPRECATED",
+    "ImpactControlNetApplyAdvancedSEGS": "ControlNetApply (SEGS)",
     "ImpactIPAdapterApplySEGS": "IPAdapterApply (SEGS)",
 
     "BboxDetectorCombined_v2": "BBOX Detector (combined)",
@@ -349,19 +313,24 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "MediaPipeFaceMeshToSEGS": "MediaPipe FaceMesh to SEGS",
     "MaskToSEGS": "MASK to SEGS",
     "MaskToSEGS_for_AnimateDiff": "MASK to SEGS for AnimateDiff",
-    "BitwiseAndMaskForEach": "Bitwise(SEGS & SEGS)",
-    "SubtractMaskForEach": "Bitwise(SEGS - SEGS)",
-    "ImpactSegsAndMask": "Bitwise(SEGS & MASK)",
-    "ImpactSegsAndMaskForEach": "Bitwise(SEGS & MASKS ForEach)",
-    "BitwiseAndMask": "Bitwise(MASK & MASK)",
-    "SubtractMask": "Bitwise(MASK - MASK)",
-    "AddMask": "Bitwise(MASK + MASK)",
+    "BitwiseAndMaskForEach": "Pixelwise(SEGS & SEGS)",
+    "SubtractMaskForEach": "Pixelwise(SEGS - SEGS)",
+    "ImpactSegsAndMask": "Pixelwise(SEGS & MASK)",
+    "ImpactSegsAndMaskForEach": "Pixelwise(SEGS & MASKS ForEach)",
+    "BitwiseAndMask": "Pixelwise(MASK & MASK)",
+    "SubtractMask": "Pixelwise(MASK - MASK)",
+    "AddMask": "Pixelwise(MASK + MASK)",
+    "MaskRectArea": "Mask Rect Area",
+    "MaskRectAreaAdvanced": "Mask Rect Area (Advanced)",
+    "ImpactFlattenMask": "Flatten Mask Batch",
     "DetailerForEach": "Detailer (SEGS)",
     "DetailerForEachPipe": "Detailer (SEGS/pipe)",
     "DetailerForEachDebug": "DetailerDebug (SEGS)",
     "DetailerForEachDebugPipe": "DetailerDebug (SEGS/pipe)",
     "SEGSDetailerForAnimateDiff": "SEGSDetailer For AnimateDiff (SEGS/pipe)",
     "DetailerForEachPipeForAnimateDiff": "Detailer For AnimateDiff (SEGS/pipe)",
+    "SEGSUpscaler": "Upscaler (SEGS)",
+    "SEGSUpscalerPipe": "Upscaler (SEGS/pipe)",
 
     "SAMDetectorCombined": "SAMDetector (combined)",
     "SAMDetectorSegmented": "SAMDetector (segmented)",
@@ -376,6 +345,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "DetailerPipeToBasicPipe": "DetailerPipe -> BasicPipe",
     "EditBasicPipe": "Edit BasicPipe",
     "EditDetailerPipe": "Edit DetailerPipe",
+    "AnyPipeToBasic": "Any PIPE -> BasicPipe",
 
     "LatentPixelScale": "Latent Scale (on Pixel Space)",
     "IterativeLatentUpscale": "Iterative Upscale (Latent/on Pixel Space)",
@@ -398,13 +368,17 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImpactSEGSToMaskBatch": "SEGS to Mask Batch",
     "ImpactSEGSPicker": "Picker (SEGS)",
     "ImpactMakeTileSEGS": "Make Tile SEGS",
+    "ImpactSEGSMerge": "SEGS Merge",
 
     "ImpactDecomposeSEGS": "Decompose (SEGS)",
     "ImpactAssembleSEGS": "Assemble (SEGS)",
     "ImpactFrom_SEG_ELT": "From SEG_ELT",
     "ImpactEdit_SEG_ELT": "Edit SEG_ELT",
+    "ImpactFrom_SEG_ELT_bbox": "From SEG_ELT bbox",
+    "ImpactFrom_SEG_ELT_crop_region": "From SEG_ELT crop_region",
     "ImpactDilate_Mask_SEG_ELT": "Dilate Mask (SEG_ELT)",
     "ImpactScaleBy_BBOX_SEG_ELT": "ScaleBy BBOX (SEG_ELT)",
+    "ImpactCount_Elts_in_SEGS": "Count Elts in SEGS",
     "ImpactDilateMask": "Dilate Mask",
     "ImpactGaussianBlurMask": "Gaussian Blur Mask",
     "ImpactDilateMaskInSEGS": "Dilate Mask (SEGS)",
@@ -417,14 +391,23 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageMaskSwitch": "Switch (images, mask)",
     "ImpactSwitch": "Switch (Any)",
     "ImpactInversedSwitch": "Inversed Switch (Any)",
+    "ImpactExecutionOrderController": "Execution Order Controller",
+    "ImpactListBridge": "List Bridge",
 
-    "MasksToMaskList": "Masks to Mask List",
-    "MaskListToMaskBatch": "Mask List to Masks",
-    "ImpactImageBatchToImageList": "Image batch to Image List",
+    "MasksToMaskList": "Mask Batch to Mask List",
+    "MaskListToMaskBatch": "Mask List to Mask Batch",
+    "ImpactImageBatchToImageList": "Image Batch to Image List",
     "ImageListToImageBatch": "Image List to Image Batch",
+
     "ImpactMakeImageList": "Make Image List",
     "ImpactMakeImageBatch": "Make Image Batch",
+    "ImpactMakeMaskList": "Make Mask List",
+    "ImpactMakeMaskBatch": "Make Mask Batch",
+    "ImpactMakeAnyList": "Make List (Any)",
+
     "ImpactStringSelector": "String Selector",
+    "StringListToString": "String List to String",
+    "WildcardPromptFromString": "Wildcard Prompt from String",
     "ImpactIsNotEmptySEGS": "SEGS isn't Empty",
     "SetDefaultImageForSEGS": "Set Default Image for SEGS",
     "RemoveImageFromSEGS": "Remove Image from SEGS",
@@ -449,7 +432,11 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "LatentSwitch": "Switch (latent/legacy)",
     "SEGSSwitch": "Switch (SEGS/legacy)",
 
-    "SEGSPreviewCNet": "SEGSPreview (CNET Image)"
+    "SEGSPreviewCNet": "SEGSPreview (CNET Image)",
+
+    "ImpactSchedulerAdapter": "Impact Scheduler Adapter",
+    "GITSSchedulerFuncProvider": "GITSScheduler Func Provider",
+    "ImpactNegativeConditioningPlaceholder": "Negative Cond Placeholder"
 }
 
 if not impact.config.get_config()['mmdet_skip']:
@@ -476,21 +463,14 @@ if not impact.config.get_config()['mmdet_skip']:
         "SegmDetectorCombined": "SegmDetectorCombined (Legacy)",
     })
 
-try:
-    import impact.subpack_nodes
 
-    NODE_CLASS_MAPPINGS.update(impact.subpack_nodes.NODE_CLASS_MAPPINGS)
-    NODE_DISPLAY_NAME_MAPPINGS.update(impact.subpack_nodes.NODE_DISPLAY_NAME_MAPPINGS)
-except Exception as e:
-    print("### ComfyUI-Impact-Pack: (IMPORT FAILED) Subpack\n")
-    print("  The module at the `custom_nodes/ComfyUI-Impact-Pack/impact_subpack` path appears to be incomplete.")
-    print("  Recommended to delete the path and restart ComfyUI.")
-    print("  If the issue persists, please report it to https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues.")
-    print("\n---------------------------------")
-    traceback.print_exc()
-    print("---------------------------------\n")
+# NOTE:  Inject directly into EXTENSION_WEB_DIRS instead of WEB_DIRECTORY
+#        Provide the js path fixed as ComfyUI-Impact-Pack instead of the path name, making it available for external use
+
+# WEB_DIRECTORY = "js"  -- deprecated method
+nodes.EXTENSION_WEB_DIRS["ComfyUI-Impact-Pack"] = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'js')
+
 
-WEB_DIRECTORY = "js"
 __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
 
 

install.py

@@ -5,7 +5,6 @@ import subprocess
 import threading
 import locale
 import traceback
-import re
 
 
 if sys.argv[0] == 'install.py':
@@ -13,10 +12,24 @@ if sys.argv[0] == 'install.py':
 
 
 impact_path = os.path.join(os.path.dirname(__file__), "modules")
-old_subpack_path = os.path.join(os.path.dirname(__file__), "subpack")
-subpack_path = os.path.join(os.path.dirname(__file__), "impact_subpack")
-subpack_repo = "https://github.com/ltdrdata/ComfyUI-Impact-Subpack"
-comfy_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
+
+
+comfy_path = os.environ.get('COMFYUI_PATH')
+if comfy_path is None:
+    print(f"\nWARN: The `COMFYUI_PATH` environment variable is not set. Assuming `{os.path.dirname(__file__)}/../../` as the ComfyUI path.", file=sys.stderr)
+    comfy_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
+
+model_path = os.environ.get('COMFYUI_MODEL_PATH')
+if model_path is None:
+    try:
+        import folder_paths
+        model_path = folder_paths.models_dir
+    except:
+        pass
+
+    if model_path is None:
+        model_path = os.path.abspath(os.path.join(comfy_path, 'models'))
+    print(f"\nWARN: The `COMFYUI_MODEL_PATH` environment variable is not set. Assuming `{model_path}` as the ComfyUI path.", file=sys.stderr)
 
 
 sys.path.append(impact_path)
@@ -34,9 +47,9 @@ def handle_stream(stream, is_stdout):
             print(msg, end="", file=sys.stderr)
             
 
-def process_wrap(cmd_str, cwd=None, handler=None):
+def process_wrap(cmd_str, cwd=None, handler=None, env=None):
     print(f"[Impact Pack] EXECUTE: {cmd_str} in '{cwd}'")
-    process = subprocess.Popen(cmd_str, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1)
+    process = subprocess.Popen(cmd_str, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env, text=True, bufsize=1)
 
     if handler is None:
         handler = handle_stream
@@ -54,222 +67,40 @@ def process_wrap(cmd_str, cwd=None, handler=None):
 # ---
 
 
-pip_list = None
-
-
-def get_installed_packages():
-    global pip_list
-
-    if pip_list is None:
-        try:
-            result = subprocess.check_output([sys.executable, '-m', 'pip', 'list'], universal_newlines=True)
-            pip_list = set([line.split()[0].lower() for line in result.split('\n') if line.strip()])
-        except subprocess.CalledProcessError as e:
-            print(f"[ComfyUI-Manager] Failed to retrieve the information of installed pip packages.")
-            return set()
-    
-    return pip_list
-    
-
-def is_installed(name):
-    name = name.strip()
-    pattern = r'([^<>!=]+)([<>!=]=?)'
-    match = re.search(pattern, name)
-    
-    if match:
-        name = match.group(1)
-        
-    result = name.lower() in get_installed_packages()
-    return result
-    
-
-def is_requirements_installed(file_path):
-    print(f"req_path: {file_path}")
-    if os.path.exists(file_path):
-        with open(file_path, 'r') as file:
-            lines = file.readlines()
-            for line in lines:
-                if not is_installed(line):
-                    return False
-                    
-    return True
-
 try:
     import platform
     import folder_paths
     from torchvision.datasets.utils import download_url
     import impact.config
 
-
     print("### ComfyUI-Impact-Pack: Check dependencies")
-
-    if "python_embeded" in sys.executable or "python_embedded" in sys.executable:
-        pip_install = [sys.executable, '-s', '-m', 'pip', 'install']
-        mim_install = [sys.executable, '-s', '-m', 'mim', 'install']
-    else:
-        pip_install = [sys.executable, '-m', 'pip', 'install']
-        mim_install = [sys.executable, '-m', 'mim', 'install']
-
-
-    def ensure_subpack():
-        import git
-        if os.path.exists(subpack_path):
-            try:
-                repo = git.Repo(subpack_path)
-                repo.remotes.origin.pull()
-            except:
-                traceback.print_exc()
-                if platform.system() == 'Windows':
-                    print(f"[ComfyUI-Impact-Pack] Please turn off ComfyUI and remove '{subpack_path}' and restart ComfyUI.")
-                else:
-                    shutil.rmtree(subpack_path)
-                    git.Repo.clone_from(subpack_repo, subpack_path)
-        else:
-            git.Repo.clone_from(subpack_repo, subpack_path)
-
-        if os.path.exists(old_subpack_path):
-            shutil.rmtree(old_subpack_path)
-
-
-    def remove_olds():
-        global comfy_path
-
-        comfy_path = os.path.dirname(folder_paths.__file__)
-        custom_nodes_path = os.path.join(comfy_path, "custom_nodes")
-        old_ini_path = os.path.join(custom_nodes_path, "impact-pack.ini")
-        old_py_path = os.path.join(custom_nodes_path, "comfyui-impact-pack.py")
-
-        if os.path.exists(impact.config.old_config_path):
-            impact.config.get_config()['mmdet_skip'] = False
-            os.remove(impact.config.old_config_path)
-
-        if os.path.exists(old_ini_path):
-            print(f"Delete legacy file: {old_ini_path}")
-            os.remove(old_ini_path)
-
-        if os.path.exists(old_py_path):
-            print(f"Delete legacy file: {old_py_path}")
-            os.remove(old_py_path)
-
-
-    def ensure_pip_packages_first():
-        subpack_req = os.path.join(subpack_path, "requirements.txt")
-        if os.path.exists(subpack_req) and not is_requirements_installed(subpack_req):
-            process_wrap(pip_install + ['-r', 'requirements.txt'], cwd=subpack_path)
-
-        if not impact.config.get_config()['mmdet_skip']:
-            process_wrap(pip_install + ['openmim'])
-
-            try:
-                import pycocotools
-            except Exception:
-                if platform.system() not in ["Windows"] or platform.machine() not in ["AMD64", "x86_64"]:
-                    print(f"Your system is {platform.system()}; !! You need to install 'libpython3-dev' for this step. !!")
-
-                    process_wrap(pip_install + ['pycocotools'])
-                else:
-                    pycocotools = {
-                        (3, 8): "https://github.com/Bing-su/dddetailer/releases/download/pycocotools/pycocotools-2.0.6-cp38-cp38-win_amd64.whl",
-                        (3, 9): "https://github.com/Bing-su/dddetailer/releases/download/pycocotools/pycocotools-2.0.6-cp39-cp39-win_amd64.whl",
-                        (3, 10): "https://github.com/Bing-su/dddetailer/releases/download/pycocotools/pycocotools-2.0.6-cp310-cp310-win_amd64.whl",
-                        (3, 11): "https://github.com/Bing-su/dddetailer/releases/download/pycocotools/pycocotools-2.0.6-cp311-cp311-win_amd64.whl",
-                    }
-
-                    version = sys.version_info[:2]
-                    url = pycocotools[version]
-                    process_wrap(pip_install + [url])
-
-
-    def ensure_pip_packages_last():
-        my_path = os.path.dirname(__file__)
-        requirements_path = os.path.join(my_path, "requirements.txt")
-        
-        if not is_requirements_installed(requirements_path):
-            process_wrap(pip_install + ['-r', requirements_path])
-            
-        # fallback
-        try:
-            import segment_anything
-            from skimage.measure import label, regionprops
-            import piexif
-        except Exception:
-            process_wrap(pip_install + ['-r', requirements_path])
-
-        # !! cv2 importing test must be very last !!
-        try:
-            import cv2
-        except Exception:
-            try:
-                if not is_installed('opencv-python'):
-                    process_wrap(pip_install + ['opencv-python'])
-                if not is_installed('opencv-python-headless'):
-                    process_wrap(pip_install + ['opencv-python-headless'])
-            except:
-                print(f"[ERROR] ComfyUI-Impact-Pack: failed to install 'opencv-python'. Please, install manually.")
-
-    def ensure_mmdet_package():
-        try:
-            import mmcv
-            import mmdet
-            from mmdet.evaluation import get_classes
-        except Exception:
-            process_wrap(pip_install + ['opendatalab==0.0.9'])
-            process_wrap(pip_install + ['-U', 'openmim'])
-            process_wrap(mim_install + ['mmcv>=2.0.0rc4, <2.1.0'])
-            process_wrap(mim_install + ['mmdet==3.0.0'])
-            process_wrap(mim_install + ['mmengine==0.7.4'])
-
-
     def install():
-        remove_olds()
-
-        subpack_install_script = os.path.join(subpack_path, "install.py")
-
-        print(f"### ComfyUI-Impact-Pack: Updating subpack")
-        try:
-            import git
-        except Exception:
-            if not is_installed('GitPython'):
-                process_wrap(pip_install + ['GitPython'])
-
-        ensure_subpack()  # The installation of the subpack must take place before ensure_pip. cv2 triggers a permission error.
-
-        if os.path.exists(subpack_install_script):
-            process_wrap([sys.executable, 'install.py'], cwd=subpack_path)
-            if not is_requirements_installed(os.path.join(subpack_path, 'requirements.txt')):
-                process_wrap(pip_install + ['-r', 'requirements.txt'], cwd=subpack_path)
-        else:
-            print(f"### ComfyUI-Impact-Pack: (Install Failed) Subpack\nFile not found: `{subpack_install_script}`")
-
-        ensure_pip_packages_first()
-
-        if not impact.config.get_config()['mmdet_skip']:
-            ensure_mmdet_package()
-
-        ensure_pip_packages_last()
+        new_env = os.environ.copy()
+        new_env["COMFYUI_PATH"] = comfy_path
+        new_env["COMFYUI_MODEL_PATH"] = model_path
 
         # Download model
         print("### ComfyUI-Impact-Pack: Check basic models")
-
-        model_path = folder_paths.models_dir
-
-        bbox_path = os.path.join(model_path, "mmdets", "bbox")
         sam_path = os.path.join(model_path, "sams")
         onnx_path = os.path.join(model_path, "onnx")
 
         if not os.path.exists(os.path.join(os.path.dirname(__file__), '..', 'skip_download_model')):
-            if not os.path.exists(bbox_path):
-                os.makedirs(bbox_path)
+            try:
+                if not impact.config.get_config()['mmdet_skip']:
+                    bbox_path = os.path.join(model_path, "mmdets", "bbox")
+                    if not os.path.exists(bbox_path):
+                        os.makedirs(bbox_path)
 
-            if not impact.config.get_config()['mmdet_skip']:
-                if not os.path.exists(os.path.join(bbox_path, "mmdet_anime-face_yolov3.pth")):
-                    download_url("https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/bbox/mmdet_anime-face_yolov3.pth", bbox_path)
+                    if not os.path.exists(os.path.join(bbox_path, "mmdet_anime-face_yolov3.pth")):
+                        download_url("https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/bbox/mmdet_anime-face_yolov3.pth", bbox_path)
 
-                if not os.path.exists(os.path.join(bbox_path, "mmdet_anime-face_yolov3.py")):
-                    download_url("https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/mmdet_anime-face_yolov3.py", bbox_path)
+                    if not os.path.exists(os.path.join(bbox_path, "mmdet_anime-face_yolov3.py")):
+                        download_url("https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/mmdet_anime-face_yolov3.py", bbox_path)
 
-            if not os.path.exists(os.path.join(sam_path, "sam_vit_b_01ec64.pth")):
-                download_url("https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth", sam_path)
+                if not os.path.exists(os.path.join(sam_path, "sam_vit_b_01ec64.pth")):
+                    download_url("https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth", sam_path)
+            except:
+                print(f"[Impact Pack] Failed to auto-download model files. Please download them manually.")
 
         if not os.path.exists(onnx_path):
             print(f"### ComfyUI-Impact-Pack: onnx model directory created ({onnx_path})")
@@ -277,6 +108,16 @@ try:
 
         impact.config.write_config()
 
+        # Remove legacy subpack
+        subpack_path = os.path.join(os.path.dirname(__file__), 'impact_subpack')
+        if os.path.exists(subpack_path):
+            shutil.rmtree(subpack_path)
+            print(f"Legacy subpack is detected. '{subpack_path}' is removed.")
+            
+        subpack_path = os.path.join(os.path.dirname(__file__), 'subpack')
+        if os.path.exists(subpack_path):
+            shutil.rmtree(subpack_path)
+            print(f"Legacy subpack is detected. '{subpack_path}' is removed.")
 
     install()
 

js/impact-image-util.js

@@ -46,7 +46,7 @@ async function loadImageFromUrl(image, node_id, v, need_to_load) {
 		if(res.status == 200) {
 			let pb_id = await res.text();
 			if(need_to_load) {;
-				image.src = `view?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`;
+				image.src = api.apiURL(`/view?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`);
 			}
 			return pb_id;
 		}
@@ -63,7 +63,7 @@ async function loadImageFromId(image, v) {
 	let res = await api.fetchApi('/impact/get/pb_id_image?id='+v, { cache: "no-store" });
 	if(res.status == 200) {
 		let item = await res.json();
-		image.src = `view?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`;
+		image.src = api.apiURL(`/view?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`);
 		return true;
 	}
 
@@ -181,7 +181,7 @@ app.registerExtension({
 
 			Object.defineProperty(node, 'imgs', {
 				set(v) {
-					if (!v[0].complete) {
+					if (v && !v[0].complete) {
 						let orig_onload = v[0].onload;
 						v[0].onload = function(v2) {
 							if(orig_onload)
@@ -209,7 +209,7 @@ app.registerExtension({
 
 							let res = api.fetchApi('/view/validate'+params, { cache: "no-store" }).then(response => response);
 							if(res.status == 200) {
-								image.src = 'view'+params;
+								image.src = api.apiURL('/view'+params);
 							}
 
 							this._img = [new Image()]; // placeholder

js/impact-pack.js

@@ -116,7 +116,27 @@ function imgSendHandler(event) {
 		let nodes = app.graph._nodes;
 		for(let i in nodes) {
 			if(nodes[i].type == 'ImageReceiver') {
-				if(nodes[i].widgets[1].value == event.detail.link_id) {
+				let is_linked = false;
+
+				if(nodes[i].widgets[1].type == 'converted-widget') {
+					for(let j in nodes[i].inputs) {
+						let input = nodes[i].inputs[j];
+						if(input.name === 'link_id') {
+							if(input.link) {
+								let src_node = app.graph._nodes_by_id[app.graph.links[input.link].origin_id];
+								if(src_node.type == 'ImpactInt' || src_node.type == 'PrimitiveNode') {
+									is_linked = true;
+								}
+							}
+							break;
+						}
+					}
+				}
+				else if(nodes[i].widgets[1].value == event.detail.link_id) {
+					is_linked = true;
+				}
+
+				if(is_linked) {
 					if(data.subfolder)
 						nodes[i].widgets[0].value = `${data.subfolder}/${data.filename} [${data.type}]`;
 					else
@@ -217,7 +237,7 @@ app.registerExtension({
 		if(nodeData.name == "ImpactControlBridge") {
 			const onConnectionsChange = nodeType.prototype.onConnectionsChange;
 			nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
-				if(!link_info || this.inputs[0].type != '*')
+				if(index != 0 || !link_info || this.inputs[0].type != '*')
 					return;
 
 				// assign type
@@ -333,7 +353,7 @@ app.registerExtension({
 						}
 
 						this.outputs[0].type = origin_type;
-						this.outputs[0].name = origin_type;
+						this.outputs[0].name = 'output1';
 					}
 
 					return;
@@ -363,7 +383,7 @@ app.registerExtension({
 				}
 
 				let select_slot = this.inputs.find(x => x.name == "select");
-				if(this.widgets) {
+				if(this.widgets?.length) {
 					this.widgets[0].options.max = select_slot?this.outputs.length-1:this.outputs.length;
 					this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max);
 					if(this.widgets[0].options.max > 0 && this.widgets[0].value == 0)
@@ -373,7 +393,8 @@ app.registerExtension({
 		}
 
 		if (nodeData.name === 'ImpactMakeImageList' || nodeData.name === 'ImpactMakeImageBatch' ||
-			nodeData.name === 'CombineRegionalPrompts' ||
+		    nodeData.name === 'ImpactMakeMaskList' || nodeData.name === 'ImpactMakeMaskBatch' ||
+			nodeData.name === 'ImpactMakeAnyList' || nodeData.name === 'CombineRegionalPrompts' ||
 			nodeData.name === 'ImpactCombineConditionings' || nodeData.name === 'ImpactConcatConditionings' ||
 			nodeData.name === 'ImpactSEGSConcat' ||
 			nodeData.name === 'ImpactSwitch' || nodeData.name === 'LatentSwitch' || nodeData.name == 'SEGSSwitch') {
@@ -385,6 +406,15 @@ app.registerExtension({
 				input_name = "image";
 				break;
 
+			case 'ImpactMakeMaskList':
+			case 'ImpactMakeMaskBatch':
+				input_name = "mask";
+				break;
+
+			case 'ImpactMakeAnyList':
+				input_name = "value";
+				break;
+
 			case 'ImpactSEGSConcat':
 				input_name = "segs";
 				break;
@@ -453,8 +483,12 @@ app.registerExtension({
 
 					if(this.inputs[0].type == '*'){
 						const node = app.graph.getNodeById(link_info.origin_id);
-						let origin_type = node.outputs[link_info.origin_slot].type;
-
+						let origin_type = node.outputs[link_info.origin_slot]?.type;
+						if(link_info.target_slot == 0 && this.inputs.length > 1) {
+								origin_type = this.inputs[1].type;
+								node.connect(link_info.origin_slot, node.id, 'input1');
+						}
+						
 						if(origin_type == '*') {
 							this.disconnectInput(link_info.target_slot);
 							return;
@@ -487,7 +521,7 @@ app.registerExtension({
 						!stackTrace.includes('LGraphNode.connect') && // for mouse device
 						!stackTrace.includes('loadGraphData') &&
 						this.inputs[index].name != 'select') {
-						this.removeInput(index);
+						    this.removeInput(index);
 					}
 				}
 
@@ -507,7 +541,7 @@ app.registerExtension({
 						this.addInput(`${input_name}${slot_i}`, this.outputs[0].type);
 				}
 
-				if(this.widgets) {
+				if(this.widgets?.length) {
 					this.widgets[0].options.max = select_slot?this.inputs.length-1:this.inputs.length;
 					this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max);
 					if(this.widgets[0].options.max > 0 && this.widgets[0].value == 0)
@@ -553,17 +587,17 @@ app.registerExtension({
 		}
 
 		if(node.comfyClass == "ImpactSEGSLabelFilter" || node.comfyClass == "SEGSLabelFilterDetailerHookProvider") {
+			node.widgets[0].callback = (value, canvas, node, pos, e) => {
+				if(node.widgets[1].value.trim() != "" && !node.widgets[1].value.trim().endsWith(","))
+					node.widgets[1].value += ", "
+
+				node.widgets[1].value += value;
+				if(node.widgets_values)
+					node.widgets_values[1] = node.widgets[1].value;
+			}
+
 			Object.defineProperty(node.widgets[0], "value", {
 				set: (value) => {
-						const stackTrace = new Error().stack;
-						if(stackTrace.includes('inner_value_change')) {
-							if(node.widgets[1].value.trim() != "" && !node.widgets[1].value.trim().endsWith(","))
-								node.widgets[1].value += ", "
-
-							node.widgets[1].value += value;
-							node.widgets_values[1] = node.widgets[1].value;
-						}
-
 						node._value = value;
 					},
 				get: () => {
@@ -578,7 +612,7 @@ app.registerExtension({
 			node.onDrawForeground = function (ctx) {
 				const r = orig_draw?.apply?.(this, arguments);
 
-				let is_seg = model_name_widget.value.startsWith('segm/') || model_name_widget.value.includes('-seg');
+				let is_seg = model_name_widget.value?.startsWith('segm/') || model_name_widget.value?.includes('-seg');
 				if(!is_seg) {
 					var slot_pos = new Float32Array(2);
 					var pos = node.getConnectionPos(false, 1, slot_pos);
@@ -633,18 +667,18 @@ app.registerExtension({
 					break;
 			}
 
+            node.widgets[combo_id+1].callback = (value, canvas, node, pos, e) => {
+                    if(node.widgets[tbox_id].value != '')
+                        node.widgets[tbox_id].value += ', '
+
+                    node.widgets[tbox_id].value += node._wildcard_value;
+            }
+
 			Object.defineProperty(node.widgets[combo_id+1], "value", {
 				set: (value) => {
-						const stackTrace = new Error().stack;
-						if(stackTrace.includes('inner_value_change')) {
-							if(value != "Select the Wildcard to add to the text") {
-								if(node.widgets[tbox_id].value != '')
-									node.widgets[tbox_id].value += ', '
-
-								node.widgets[tbox_id].value += value;
-							}
-						}
-					},
+                    if (value !== "Select the Wildcard to add to the text")
+                        node._wildcard_value = value;
+                },
 				get: () => { return "Select the Wildcard to add to the text"; }
 			});
 
@@ -656,24 +690,22 @@ app.registerExtension({
 			});
 
 			if(has_lora) {
+				node.widgets[combo_id].callback = (value, canvas, node, pos, e) => {
+					let lora_name = node._value;
+					if(lora_name.endsWith('.safetensors')) {
+						lora_name = lora_name.slice(0, -12);
+					}
+
+					node.widgets[tbox_id].value += `<lora:${lora_name}>`;
+					if(node.widgets_values) {
+						node.widgets_values[tbox_id] = node.widgets[tbox_id].value;
+					}
+				}
+
 				Object.defineProperty(node.widgets[combo_id], "value", {
 					set: (value) => {
-							const stackTrace = new Error().stack;
-							if(stackTrace.includes('inner_value_change')) {
-								if(value != "Select the LoRA to add to the text") {
-									let lora_name = value;
-									if (lora_name.endsWith('.safetensors')) {
-										lora_name = lora_name.slice(0, -12);
-									}
-
-									node.widgets[tbox_id].value += `<lora:${lora_name}>`;
-									if(node.widgets_values) {
-										node.widgets_values[tbox_id] = node.widgets[tbox_id].value;
-									}
-								}
-							}
-
-							node._value = value;
+							if (value !== "Select the LoRA to add to the text")
+								node._value = value;
 						},
 
 					get: () => { return "Select the LoRA to add to the text"; }

js/impact-sam-editor.js

@@ -1,4 +1,5 @@
 import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js";
 import { ComfyDialog, $el } from "../../scripts/ui.js";
 import { ComfyApp } from "../../scripts/app.js";
 import { ClipspaceDialog } from "../../extensions/core/clipspace.js";
@@ -42,7 +43,7 @@ function loadedImageToBlob(image) {
 }
 
 async function uploadMask(filepath, formData) {
-	await fetch('/upload/mask', {
+	await api.fetchApi('/upload/mask', {
 		method: 'POST',
 		body: formData
 	}).then(response => {}).catch(error => {
@@ -261,7 +262,7 @@ class ImpactSamEditorDialog extends ComfyDialog {
 			const pointsCanvas = document.createElement('canvas');
 
 			imgCanvas.id = "imageCanvas";
-			maskCanvas.id = "maskCanvas";
+			maskCanvas.id = "samEditorMaskCanvas";
 			pointsCanvas.id = "pointsCanvas";
 
 			this.setlayout(imgCanvas, maskCanvas, pointsCanvas);
@@ -352,13 +353,16 @@ class ImpactSamEditorDialog extends ComfyDialog {
 			imgCtx.drawImage(orig_image, 0, 0, drawWidth, drawHeight);
 
 			// update mask
-			pointsCanvas.width = drawWidth;
-			pointsCanvas.height = drawHeight;
+			let w = (drawWidth * imgCanvas.clientWidth/imgCanvas.width) + "px";
+			let h = (drawHeight * imgCanvas.clientHeight/imgCanvas.height) + "px";
+
+			pointsCanvas.width = drawWidth * imgCanvas.clientWidth/imgCanvas.width;
+			pointsCanvas.height = drawHeight * imgCanvas.clientHeight/imgCanvas.height;
 			pointsCanvas.style.top = imgCanvas.offsetTop + "px";
 			pointsCanvas.style.left = imgCanvas.offsetLeft + "px";
 
-			maskCanvas.width = drawWidth;
-			maskCanvas.height = drawHeight;
+			maskCanvas.width = pointsCanvas.width;
+			maskCanvas.height = pointsCanvas.height;
 			maskCanvas.style.top = imgCanvas.offsetTop + "px";
 			maskCanvas.style.left = imgCanvas.offsetLeft + "px";
 
@@ -434,7 +438,7 @@ class ImpactSamEditorDialog extends ComfyDialog {
 			ctx.arc(scaledX, scaledY, 3, 0, 3 * Math.PI);
 			ctx.fill();
 		}
-	}줘
+	}
 
 	invalidateMaskCanvas(self) {
 		if(self.mask_image) {
@@ -458,7 +462,7 @@ class ImpactSamEditorDialog extends ComfyDialog {
 				subfolder: subfolder
 			};
 
-		fetch('/sam/prepare', {
+		api.fetchApi('/sam/prepare', {
 			method: 'POST',
 			headers: { 'Content-Type': 'application/json' },
 			body: JSON.stringify(data)
@@ -472,8 +476,9 @@ class ImpactSamEditorDialog extends ComfyDialog {
 		for(const i in self.prompt_points) {
 			const [is_positive, x, y] = self.prompt_points[i];
 			const point = [x,y];
-			if(is_positive)
+			if(is_positive) {
 				positive_points.push(point);
+			}
 			else
 				negative_points.push(point);
 		}
@@ -484,7 +489,7 @@ class ImpactSamEditorDialog extends ComfyDialog {
 			threshold: self.confidence/100
 		};
 		
-		const response = await fetch('/sam/detect', {
+		const response = await api.fetchApi('/sam/detect', {
 			method: 'POST',
 			headers: { 'Content-Type': 'image/png' },
 			body: JSON.stringify(data)
@@ -507,8 +512,8 @@ class ImpactSamEditorDialog extends ComfyDialog {
 			const x = event.offsetX || event.targetTouches[0].clientX - maskRect.left;
 			const y = event.offsetY || event.targetTouches[0].clientY - maskRect.top;
 
-			const originalX = x * self.image.width / self.pointsCanvas.width;
-			const originalY = y * self.image.height / self.pointsCanvas.height;
+			const originalX = x * self.image.width / self.pointsCanvas.clientWidth;
+			const originalY = y * self.image.height / self.pointsCanvas.clientHeight;
 
 			var point = null;
 			if (event.button == 0) {

js/impact-wildcard.js

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

js/mask-rect-area-advanced.js

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

js/mask-rect-area.js

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

modules/impact/animatediff_nodes.py

@@ -5,28 +5,35 @@ from impact.core import SEG
 from impact.segs_nodes import SEGSPaste
 
 
+try:
+    from comfy_extras import nodes_differential_diffusion
+except Exception:
+    print(f"\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n")
+    raise Exception("[Impact Pack] ComfyUI is an outdated version.")
+
+
 class SEGSDetailerForAnimateDiff:
     @classmethod
     def INPUT_TYPES(cls):
         return {"required": {
                      "image_frames": ("IMAGE", ),
                      "segs": ("SEGS", ),
-                     "guide_size": ("FLOAT", {"default": 256, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
+                     "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
                      "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
                      "max_size": ("FLOAT", {"default": 768, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
                      "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                      "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                      "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                      "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
-                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
+                     "scheduler": (core.SCHEDULERS,),
                      "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
                      "basic_pipe": ("BASIC_PIPE",),
-                     "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0})
+                     "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
                      },
                 "optional": {
                      "refiner_basic_pipe_opt": ("BASIC_PIPE",),
-                     # TODO: "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
-                     # TODO: "noise_mask_feather": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
+                     "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
+                     "scheduler_func_opt": ("SCHEDULER_FUNC",),
                      }
                 }
 
@@ -40,7 +47,7 @@ class SEGSDetailerForAnimateDiff:
 
     @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, inpaint_model=False, noise_mask_feather=0):
+                  denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, noise_mask_feather=0, scheduler_func_opt=None):
 
         model, clip, vae, positive, negative = basic_pipe
         if refiner_basic_pipe_opt is None:
@@ -53,6 +60,9 @@ class SEGSDetailerForAnimateDiff:
         new_segs = []
         cnet_image_list = []
 
+        if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
+            model = nodes_differential_diffusion.DifferentialDiffusion().apply(model)[0]
+
         for seg in segs[1]:
             cropped_image_frames = None
 
@@ -66,13 +76,31 @@ class SEGSDetailerForAnimateDiff:
                     cropped_image_frames = torch.concat((cropped_image_frames, cropped_image), dim=0)
 
             cropped_image_frames = cropped_image_frames.cpu().numpy()
+
+            # It is assumed that AnimateDiff does not support conditioning masks based on test results, but it will be added for future consideration.
+            cropped_positive = [
+                [condition, {
+                    k: core.crop_condition_mask(v, cropped_image_frames, seg.crop_region) if k == "mask" else v
+                    for k, v in details.items()
+                }]
+                for condition, details in positive
+            ]
+
+            cropped_negative = [
+                [condition, {
+                    k: core.crop_condition_mask(v, cropped_image_frames, seg.crop_region) if k == "mask" else v
+                    for k, v in details.items()
+                }]
+                for condition, details in negative
+            ]
+
             enhanced_image_tensor, cnet_images = core.enhance_detail_for_animatediff(cropped_image_frames, model, clip, vae, guide_size, guide_size_for, max_size,
                                                                                      seg.bbox, seed, steps, cfg, sampler_name, scheduler,
-                                                                                     positive, negative, denoise, seg.cropped_mask,
+                                                                                     cropped_positive, cropped_negative, denoise, seg.cropped_mask,
                                                                                      refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                                                                                      refiner_clip=refiner_clip, refiner_positive=refiner_positive,
                                                                                      refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper,
-                                                                                     inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
+                                                                                     noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt)
             if cnet_images is not None:
                 cnet_image_list.extend(cnet_images)
 
@@ -87,11 +115,11 @@ class SEGSDetailerForAnimateDiff:
         return (segs[0], new_segs), cnet_image_list
 
     def doit(self, 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, inpaint_model=False, noise_mask_feather=0):
+             denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
 
         segs, cnet_images = SEGSDetailerForAnimateDiff.do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
                                                                  scheduler, denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt,
-                                                                 inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
+                                                                 noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
 
         if len(cnet_images) == 0:
             cnet_images = [empty_pil_tensor()]
@@ -105,25 +133,25 @@ class DetailerForEachPipeForAnimateDiff:
         return {"required": {
                       "image_frames": ("IMAGE", ),
                       "segs": ("SEGS", ),
-                      "guide_size": ("FLOAT", {"default": 384, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                      "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
                       "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
                       "max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
                       "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                       "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                       "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                       "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
-                      "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
+                      "scheduler": (core.SCHEDULERS,),
                       "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
                       "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
                       "basic_pipe": ("BASIC_PIPE", ),
                       "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
-                     },
+                      },
                 "optional": {
-                     "detailer_hook": ("DETAILER_HOOK",),
-                     "refiner_basic_pipe_opt": ("BASIC_PIPE",),
-                     # "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
-                     # "noise_mask_feather": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
-                    }
+                      "detailer_hook": ("DETAILER_HOOK",),
+                      "refiner_basic_pipe_opt": ("BASIC_PIPE",),
+                      "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
+                      "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                      }
                 }
 
     RETURN_TYPES = ("IMAGE", "SEGS", "BASIC_PIPE", "IMAGE")
@@ -136,7 +164,7 @@ class DetailerForEachPipeForAnimateDiff:
     @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,
-             inpaint_model=False, noise_mask_feather=0):
+             noise_mask_feather=0, scheduler_func_opt=None):
 
         enhanced_segs = []
         cnet_image_list = []
@@ -144,7 +172,7 @@ class DetailerForEachPipeForAnimateDiff:
         for sub_seg in segs[1]:
             single_seg = segs[0], [sub_seg]
             enhanced_seg, cnet_images = SEGSDetailerForAnimateDiff().do_detail(image_frames, single_seg, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
-                                                                               denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt, inpaint_model, noise_mask_feather)
+                                                                               denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt, noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
 
             image_frames = SEGSPaste.doit(image_frames, enhanced_seg, feather, alpha=255)[0]
 
@@ -152,7 +180,7 @@ class DetailerForEachPipeForAnimateDiff:
                 cnet_image_list.extend(cnet_images)
 
             if detailer_hook is not None:
-                detailer_hook.post_paste(image_frames)
+                image_frames = detailer_hook.post_paste(image_frames)
 
             enhanced_segs += enhanced_seg[1]
 

modules/impact/bridge_nodes.py

@@ -1,10 +1,17 @@
 import os
 from PIL import ImageOps
 from impact.utils import *
+import latent_preview
 
-from . import core
+
+# NOTE: this should not be `from . import core`.
+# I don't know why but... 'from .' and 'from impact' refer to different core modules.
+# This separates global variables of the core module and breaks the preview bridge.
+from impact import core
+# <--
 import random
 
+
 class PreviewBridge:
     @classmethod
     def INPUT_TYPES(s):
@@ -12,7 +19,11 @@ class PreviewBridge:
                     "images": ("IMAGE",),
                     "image": ("STRING", {"default": ""}),
                     },
-                "hidden": {"unique_id": "UNIQUE_ID"},
+                "optional": {
+                    "block": ("BOOLEAN", {"default": False, "label_on": "if_empty_mask", "label_off": "never", "tooltip": "is_empty_mask: If the mask is empty, the execution is stopped.\nnever: The execution is never stopped."}),
+                    "restore_mask": (["never", "always", "if_same_size"], {"tooltip": "if_same_size: If the changed input image is the same size as the previous image, restore using the last saved mask\nalways: Whenever the input image changes, always restore using the last saved mask\nnever: Do not restore the mask.\n`restore_mask` has higher priority than `block`"}),
+                    },
+                "hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO"},
                 }
 
     RETURN_TYPES = ("IMAGE", "MASK", )
@@ -23,6 +34,8 @@ class PreviewBridge:
 
     CATEGORY = "ImpactPack/Util"
 
+    DESCRIPTION = "This is a feature that allows you to edit and send a Mask over a image.\nIf the block is set to 'is_empty_mask', the execution is stopped when the mask is empty."
+
     def __init__(self):
         super().__init__()
         self.output_dir = folder_paths.get_temp_directory()
@@ -63,7 +76,7 @@ class PreviewBridge:
 
         return image, mask.unsqueeze(0), ui_item
 
-    def doit(self, images, image, unique_id):
+    def doit(self, images, image, unique_id, block=False, restore_mask="never", prompt=None, extra_pnginfo=None):
         need_refresh = False
 
         if unique_id not in core.preview_bridge_cache:
@@ -76,10 +89,25 @@ class PreviewBridge:
             pixels, mask, path_item = PreviewBridge.load_image(image)
             image = [path_item]
         else:
-            res = nodes.PreviewImage().save_images(images, filename_prefix="PreviewBridge/PB-")
+            if restore_mask != "never":
+                mask = core.preview_bridge_last_mask_cache.get(unique_id)
+                if mask is None or (restore_mask != "always" and mask.shape[1:] != images.shape[1:3]):
+                    mask = None
+            else:
+                mask = None
+
+            if mask is None:
+                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+                res = nodes.PreviewImage().save_images(images, filename_prefix="PreviewBridge/PB-", prompt=prompt, extra_pnginfo=extra_pnginfo)
+            else:
+                masked_images = tensor_convert_rgba(images)
+                resized_mask = resize_mask(mask, (images.shape[1], images.shape[2])).unsqueeze(3)
+                resized_mask = 1 - resized_mask
+                tensor_putalpha(masked_images, resized_mask)
+                res = nodes.PreviewImage().save_images(masked_images, filename_prefix="PreviewBridge/PB-", prompt=prompt, extra_pnginfo=extra_pnginfo)
+
             image2 = res['ui']['images']
             pixels = images
-            mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
 
             path = os.path.join(folder_paths.get_temp_directory(), 'PreviewBridge', image2[0]['filename'])
             core.set_previewbridge_image(unique_id, path, image2[0])
@@ -89,44 +117,91 @@ class PreviewBridge:
 
             image = image2
 
+        is_empty_mask = torch.all(mask == 0)
+
+        if block and is_empty_mask and core.is_execution_model_version_supported():
+            from comfy_execution.graph import ExecutionBlocker
+            result = ExecutionBlocker(None), ExecutionBlocker(None)
+        elif block and is_empty_mask:
+            print(f"[Impact Pack] PreviewBridge: ComfyUI is outdated - blocking feature is disabled.")
+            result = pixels, mask
+        else:
+            result = pixels, mask
+
+        if not is_empty_mask:
+            core.preview_bridge_last_mask_cache[unique_id] = mask
+
         return {
             "ui": {"images": image},
-            "result": (pixels, mask, ),
+            "result": result,
         }
 
 
-def decode_latent(latent_tensor, preview_method, vae_opt=None):
+def decode_latent(latent, preview_method, vae_opt=None):
     if vae_opt is not None:
-        image = nodes.VAEDecode().decode(vae_opt, latent_tensor)[0]
+        image = nodes.VAEDecode().decode(vae_opt, latent)[0]
         return image
 
     from comfy.cli_args import LatentPreviewMethod
     import comfy.latent_formats as latent_formats
 
     if preview_method.startswith("TAE"):
+        decoder_name = None
+
         if preview_method == "TAESD15":
             decoder_name = "taesd"
-        else:
+        elif preview_method == 'TAESDXL':
             decoder_name = "taesdxl"
+        elif preview_method == 'TAESD3':
+            decoder_name = "taesd3"
+        elif preview_method == 'TAEF1':
+            decoder_name = "taef1"
 
-        vae = nodes.VAELoader().load_vae(decoder_name)[0]
-        image = nodes.VAEDecode().decode(vae, latent_tensor)[0]
-        return image
+        if decoder_name:
+            vae = nodes.VAELoader().load_vae(decoder_name)[0]
+            image = nodes.VAEDecode().decode(vae, latent)[0]
+            return image
 
+    if preview_method == "Latent2RGB-SD15":
+        latent_format = latent_formats.SD15()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-SDXL":
+        latent_format = latent_formats.SDXL()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-SD3":
+        latent_format = latent_formats.SD3()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-SD-X4":
+        latent_format = latent_formats.SD_X4()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-Playground-2.5":
+        latent_format = latent_formats.SDXL_Playground_2_5()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-SC-Prior":
+        latent_format = latent_formats.SC_Prior()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-SC-B":
+        latent_format = latent_formats.SC_B()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-FLUX.1":
+        latent_format = latent_formats.Flux()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-LTXV":
+        latent_format = latent_formats.LTXV()
+        method = LatentPreviewMethod.Latent2RGB
     else:
-        if preview_method == "Latent2RGB-SD15":
-            latent_format = latent_formats.SD15()
-            method = LatentPreviewMethod.Latent2RGB
-        else:  # preview_method == "Latent2RGB-SDXL"
-            latent_format = latent_formats.SDXL()
-            method = LatentPreviewMethod.Latent2RGB
+        print(f"[Impact Pack] PreviewBridgeLatent: '{preview_method}' is unsupported preview method.")
+        latent_format = latent_formats.SD15()
+        method = LatentPreviewMethod.Latent2RGB
 
-        previewer = core.get_previewer("cpu", latent_format=latent_format, force=True, method=method)
-        pil_image = previewer.decode_latent_to_preview(latent_tensor['samples'])
-        pixels_size = pil_image.size[0]*8, pil_image.size[1]*8
-        resized_image = pil_image.resize(pixels_size, Image.NONE)
+    previewer = core.get_previewer("cpu", latent_format=latent_format, force=True, method=method)
+    samples = latent_format.process_in(latent['samples'])
 
-        return to_tensor(resized_image).unsqueeze(0)
+    pil_image = previewer.decode_latent_to_preview(samples)
+    pixels_size = pil_image.size[0]*8, pil_image.size[1]*8
+    resized_image = pil_image.resize(pixels_size, resample=LANCZOS)
+
+    return to_tensor(resized_image).unsqueeze(0)
 
 
 class PreviewBridgeLatent:
@@ -135,12 +210,19 @@ class PreviewBridgeLatent:
         return {"required": {
                     "latent": ("LATENT",),
                     "image": ("STRING", {"default": ""}),
-                    "preview_method": (["Latent2RGB-SDXL", "Latent2RGB-SD15", "TAESDXL", "TAESD15"],),
+                    "preview_method": (["Latent2RGB-FLUX.1",
+                                        "Latent2RGB-SDXL", "Latent2RGB-SD15", "Latent2RGB-SD3",
+                                        "Latent2RGB-SD-X4", "Latent2RGB-Playground-2.5",
+                                        "Latent2RGB-SC-Prior", "Latent2RGB-SC-B",
+                                        "Latent2RGB-LTXV",
+                                        "TAEF1", "TAESDXL", "TAESD15", "TAESD3"],),
                     },
                 "optional": {
-                    "vae_opt": ("VAE", )
+                    "vae_opt": ("VAE", ),
+                    "block": ("BOOLEAN", {"default": False, "label_on": "if_empty_mask", "label_off": "never", "tooltip": "is_empty_mask: If the mask is empty, the execution is stopped.\nnever: The execution is never stopped. Instead, it returns a white mask."}),
+                    "restore_mask": (["never", "always", "if_same_size"], {"tooltip": "if_same_size: If the changed input latent is the same size as the previous latent, restore using the last saved mask\nalways: Whenever the input latent changes, always restore using the last saved mask\nnever: Do not restore the mask.\n`restore_mask` has higher priority than `block`\nIf the input latent already has a mask, do not restore mask."}),
                 },
-                "hidden": {"unique_id": "UNIQUE_ID"},
+                "hidden": {"unique_id": "UNIQUE_ID", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                 }
 
     RETURN_TYPES = ("LATENT", "MASK", )
@@ -151,6 +233,8 @@ class PreviewBridgeLatent:
 
     CATEGORY = "ImpactPack/Util"
 
+    DESCRIPTION = "This is a feature that allows you to edit and send a Mask over a latent image.\nIf the block is set to 'is_empty_mask', the execution is stopped when the mask is empty."
+
     def __init__(self):
         super().__init__()
         self.output_dir = folder_paths.get_temp_directory()
@@ -192,7 +276,20 @@ class PreviewBridgeLatent:
 
         return image, mask, ui_item
 
-    def doit(self, latent, image, preview_method, vae_opt=None, unique_id=None):
+    def doit(self, latent, image, preview_method, vae_opt=None, block=False, unique_id=None, restore_mask='never', prompt=None, extra_pnginfo=None):
+        latent_channels = latent['samples'].shape[1]
+
+        if 'SD3' in preview_method or 'SC-Prior' in preview_method or 'FLUX.1' in preview_method or 'TAEF1' == preview_method:
+            preview_method_channels = 16
+        elif 'LTXV' in preview_method:
+            preview_method_channels = 128
+        else:
+            preview_method_channels = 4
+
+        if vae_opt is None and latent_channels != preview_method_channels:
+            print(f"[PreviewBridgeLatent] The version of latent is not compatible with preview_method.\nSD3, SD1/SD2, SDXL, SC-Prior, SC-B and FLUX.1 are not compatible with each other.")
+            raise Exception("The version of latent is not compatible with preview_method.<BR>SD3, SD1/SD2, SDXL, SC-Prior, SC-B and FLUX.1 are not compatible with each other.")
+
         need_refresh = False
 
         if unique_id not in core.preview_bridge_cache:
@@ -214,16 +311,20 @@ class PreviewBridgeLatent:
                     del res_latent['noise_mask']
                 else:
                     res_latent = latent
+
+                is_empty_mask = True
             else:
                 res_latent = latent.copy()
                 res_latent['noise_mask'] = mask
 
+                is_empty_mask = torch.all(mask == 1)
+
             res_image = [path_item]
         else:
             decoded_image = decode_latent(latent, preview_method, vae_opt)
 
             if 'noise_mask' in latent:
-                mask = latent['noise_mask']
+                mask = latent['noise_mask'].squeeze(0)  # 4D mask -> 3D mask
 
                 decoded_pil = to_pil(decoded_image)
 
@@ -239,11 +340,30 @@ class PreviewBridgeLatent:
                                 'subfolder': 'PreviewBridge',
                                 'type': 'temp',
                             }]
+
+                is_empty_mask = False
             else:
-                mask = torch.ones(latent['samples'].shape[2:], dtype=torch.float32, device="cpu").unsqueeze(0)
-                res = nodes.PreviewImage().save_images(decoded_image, filename_prefix="PreviewBridge/PBL-")
+                if restore_mask != "never":
+                    mask = core.preview_bridge_last_mask_cache.get(unique_id)
+                    if mask is None or (restore_mask != "always" and mask.shape[1:] != decoded_image.shape[1:3]):
+                        mask = None
+                else:
+                    mask = None
+
+                if mask is None:
+                    mask = torch.ones(latent['samples'].shape[2:], dtype=torch.float32, device="cpu").unsqueeze(0)
+                    res = nodes.PreviewImage().save_images(decoded_image, filename_prefix="PreviewBridge/PBL-", prompt=prompt, extra_pnginfo=extra_pnginfo)
+                else:
+                    masked_images = tensor_convert_rgba(decoded_image)
+                    resized_mask = resize_mask(mask, (decoded_image.shape[1], decoded_image.shape[2])).unsqueeze(3)
+                    resized_mask = 1 - resized_mask
+                    tensor_putalpha(masked_images, resized_mask)
+                    res = nodes.PreviewImage().save_images(masked_images, filename_prefix="PreviewBridge/PBL-", prompt=prompt, extra_pnginfo=extra_pnginfo)
+
                 res_image = res['ui']['images']
 
+            is_empty_mask = torch.all(mask == 1)
+
             path = os.path.join(folder_paths.get_temp_directory(), 'PreviewBridge', res_image[0]['filename'])
             core.set_previewbridge_image(unique_id, path, res_image[0])
             core.preview_bridge_image_id_map[image] = (path, res_image[0])
@@ -252,7 +372,19 @@ class PreviewBridgeLatent:
 
             res_latent = latent
 
+        if block and is_empty_mask and core.is_execution_model_version_supported():
+            from comfy_execution.graph import ExecutionBlocker
+            result = ExecutionBlocker(None), ExecutionBlocker(None)
+        elif block and is_empty_mask:
+            print(f"[Impact Pack] PreviewBridgeLatent: ComfyUI is outdated - blocking feature is disabled.")
+            result = res_latent, mask
+        else:
+            result = res_latent, mask
+
+        if not is_empty_mask:
+            core.preview_bridge_last_mask_cache[unique_id] = mask
+
         return {
             "ui": {"images": res_image},
-            "result": (res_latent, mask, ),
+            "result": result,
         }

modules/impact/config.py

@@ -1,19 +1,16 @@
 import configparser
 import os
 
-
-version_code = [4, 81]
+version_code = [8, 1, 1]
 version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
 
-dependency_version = 20
+dependency_version = 24
 
 my_path = os.path.dirname(__file__)
 old_config_path = os.path.join(my_path, "impact-pack.ini")
 config_path = os.path.join(my_path, "..", "..", "impact-pack.ini")
 latent_letter_path = os.path.join(my_path, "..", "..", "latent.png")
 
-MAX_RESOLUTION = 8192
-
 
 def write_config():
     config = configparser.ConfigParser()
@@ -35,11 +32,15 @@ def read_config():
         config.read(config_path)
         default_conf = config['default']
 
+        if not os.path.exists(default_conf['custom_wildcards']):
+            print(f"[WARN] ComfyUI-Impact-Pack: custom_wildcards path not found: {default_conf['custom_wildcards']}. Using default path.")
+            default_conf['custom_wildcards'] = os.path.join(my_path, "..", "..", "custom_wildcards")
+
         return {
                     'dependency_version': int(default_conf['dependency_version']),
                     'mmdet_skip': default_conf['mmdet_skip'].lower() == 'true' if 'mmdet_skip' in default_conf else True,
                     'sam_editor_cpu': default_conf['sam_editor_cpu'].lower() == 'true' if 'sam_editor_cpu' in default_conf else False,
-                    'sam_editor_model': 'sam_vit_b_01ec64.pth',
+                    'sam_editor_model': default_conf['sam_editor_model'].lower() if 'sam_editor_model' else 'sam_vit_b_01ec64.pth',
                     'custom_wildcards': default_conf['custom_wildcards'] if 'custom_wildcards' in default_conf else os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "custom_wildcards")),
                     'disable_gpu_opencv': default_conf['disable_gpu_opencv'].lower() == 'true' if 'disable_gpu_opencv' in default_conf else True
                }

modules/impact/core.py

@@ -1,10 +1,17 @@
+import copy
+import os
+import warnings
+
+import numpy
 import torch
 from segment_anything import SamPredictor
 
+from comfy_extras.nodes_custom_sampler import Noise_RandomNoise
 from impact.utils import *
 from collections import namedtuple
 import numpy as np
 from skimage.measure import label
+from PIL import ImageOps
 
 import nodes
 import comfy_extras.nodes_upscale_model as model_upscale
@@ -18,7 +25,14 @@ from comfy import model_management
 from impact import utils
 from impact import impact_sampling
 from concurrent.futures import ThreadPoolExecutor
-from comfy.ldm.cascade.stage_c_coder import StageC_coder
+import inspect
+
+
+try:
+    from comfy_extras import nodes_differential_diffusion
+except Exception:
+    print(f"\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n")
+    raise Exception("[Impact Pack] ComfyUI is an outdated version.")
 
 
 SEG = namedtuple("SEG",
@@ -28,7 +42,21 @@ SEG = namedtuple("SEG",
 pb_id_cnt = time.time()
 preview_bridge_image_id_map = {}
 preview_bridge_image_name_map = {}
+
 preview_bridge_cache = {}
+preview_bridge_last_mask_cache = {}
+
+current_prompt = None
+
+SCHEDULERS = comfy.samplers.KSampler.SCHEDULERS + ['AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]', 'LTXV[default]']
+
+
+def is_execution_model_version_supported():
+    try:
+        import comfy_execution
+        return True
+    except:
+        return False
 
 
 def set_previewbridge_image(node_id, file, item):
@@ -42,6 +70,13 @@ def set_previewbridge_image(node_id, file, item):
     pb_id = f"${node_id}-{pb_id_cnt}"
     preview_bridge_image_id_map[pb_id] = (file, item)
     preview_bridge_image_name_map[node_id, file] = (pb_id, item)
+    if os.path.isfile(file):
+        i = Image.open(file)
+        i = ImageOps.exif_transpose(i)
+        if 'A' in i.getbands():
+            mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+            mask = 1. - torch.from_numpy(mask)
+            preview_bridge_last_mask_cache[node_id] = mask.unsqueeze(0)
     pb_id_cnt += 1
 
     return pb_id
@@ -67,19 +102,60 @@ def erosion_mask(mask, grow_mask_by):
     return mask_erosion[:, :, :w, :h].round().cpu()
 
 
+# CREDIT: https://github.com/BlenderNeko/ComfyUI_Noise/blob/afb14757216257b12268c91845eac248727a55e2/nodes.py#L68
+#         https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
+def slerp(val, low, high):
+    dims = low.shape
+
+    low = low.reshape(dims[0], -1)
+    high = high.reshape(dims[0], -1)
+
+    low_norm = low/torch.norm(low, dim=1, keepdim=True)
+    high_norm = high/torch.norm(high, dim=1, keepdim=True)
+
+    low_norm[low_norm != low_norm] = 0.0
+    high_norm[high_norm != high_norm] = 0.0
+
+    omega = torch.acos((low_norm*high_norm).sum(1))
+    so = torch.sin(omega)
+    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+
+    return res.reshape(dims)
+
+
+def mix_noise(from_noise, to_noise, strength, variation_method):
+    if variation_method == 'slerp':
+        mixed_noise = slerp(strength, from_noise, to_noise)
+    else:
+        # linear
+        mixed_noise = (1 - strength) * from_noise + strength * to_noise
+
+        # NOTE: Since the variance of the Gaussian noise in mixed_noise has changed, it must be corrected through scaling.
+        scale_factor = math.sqrt((1 - strength) ** 2 + strength ** 2)
+        mixed_noise /= scale_factor
+
+    return mixed_noise
+
+
 class REGIONAL_PROMPT:
-    def __init__(self, mask, sampler):
+    def __init__(self, mask, sampler, variation_seed=0, variation_strength=0.0, variation_method='linear'):
         mask = make_2d_mask(mask)
 
         self.mask = mask
         self.sampler = sampler
         self.mask_erosion = None
         self.erosion_factor = None
+        self.variation_seed = variation_seed
+        self.variation_strength = variation_strength
+        self.variation_method = variation_method
 
     def clone_with_sampler(self, sampler):
         rp = REGIONAL_PROMPT(self.mask, sampler)
         rp.mask_erosion = self.mask_erosion
         rp.erosion_factor = self.erosion_factor
+        rp.variation_seed = self.variation_seed
+        rp.variation_strength = self.variation_strength
+        rp.variation_method = self.variation_method
         return rp
 
     def get_mask_erosion(self, factor):
@@ -89,6 +165,18 @@ class REGIONAL_PROMPT:
 
         return self.mask_erosion
 
+    def touch_noise(self, noise):
+        if self.variation_strength > 0.0:
+            mask = utils.make_3d_mask(self.mask)
+            mask = utils.resize_mask(mask, (noise.shape[2], noise.shape[3])).unsqueeze(0)
+
+            regional_noise = Noise_RandomNoise(self.variation_seed).generate_noise({'samples': noise})
+            mixed_noise = mix_noise(noise, regional_noise, self.variation_strength, variation_method=self.variation_method)
+
+            return (mask == 1).float() * mixed_noise + (mask == 0).float() * noise
+
+        return noise
+
 
 class NO_BBOX_DETECTOR:
     pass
@@ -156,12 +244,15 @@ 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):
+                   inpaint_model=False, noise_mask_feather=0, scheduler_func=None):
 
     if noise_mask is not None:
         noise_mask = utils.tensor_gaussian_blur_mask(noise_mask, noise_mask_feather)
         noise_mask = noise_mask.squeeze(3)
 
+        if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
+            model = nodes_differential_diffusion.DifferentialDiffusion().apply(model)[0]
+
     if wildcard_opt is not None and wildcard_opt != "":
         model, _, wildcard_positive = wildcards.process_with_loras(wildcard_opt, model, clip)
 
@@ -169,6 +260,11 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
             positive = nodes.ConditioningConcat().concat(positive, wildcard_positive)[0]
         else:
             positive = wildcard_positive
+            positive = [positive[0].copy()]
+            if 'pooled_output' in wildcard_positive[0][1]:
+                positive[0][1]['pooled_output'] = wildcard_positive[0][1]['pooled_output']
+            elif 'pooled_output' in positive[0][1]:
+                del positive[0][1]['pooled_output']
 
     h = image.shape[1]
     w = image.shape[2]
@@ -215,20 +311,6 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
             new_w = w
             new_h = h
 
-    is_stable_cascade_mode = isinstance(vae.first_stage_model, StageC_coder)
-
-    if is_stable_cascade_mode:
-        dw = new_w % 8
-        dh = new_h % 8
-
-        # preserve aspect ratio as possible
-        if dw > 3 or dh > 3:
-            new_w += 8 - dw
-            new_h += 8 - dh
-        elif dw > 0 or dh > 0:
-            new_w -= dw
-            new_h -= dh
-
     if detailer_hook is not None:
         new_w, new_h = detailer_hook.touch_scaled_size(new_w, new_h)
 
@@ -245,16 +327,14 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
 
     # prepare mask
     if noise_mask is not None and inpaint_model:
-        positive, negative, latent_image = nodes.InpaintModelConditioning().encode(positive, negative, upscaled_image, vae, noise_mask)
-    else:
-        if is_stable_cascade_mode:
-            latent_image = detailer_hook.stable_cascade_vae_encode(vae, upscaled_image)
-            if latent_image is None:
-                print(f"[Impact Pack] When using the StableCascade model, it is necessary to connect the StableCascade_DetailerHook.")
-                raise Exception("StableCascade_DetailerHook is not provided.")
+        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:
-            latent_image = to_latent_image(upscaled_image, vae)
-
+            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)
         if noise_mask is not None:
             latent_image['noise_mask'] = noise_mask
 
@@ -273,24 +353,28 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
 
             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)
+                                                          refined_latent, denoise2, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative,
+                                                          noise=noise, scheduler_func=scheduler_func)
 
-    # non-latent downscale - latent downscale cause bad quality
     if detailer_hook is not None:
         refined_latent = detailer_hook.pre_decode(refined_latent)
-        stage_b = detailer_hook.stable_cascade_stage_b(image, positive, negative, refined_latent)
-    else:
-        stage_b = None
 
-    if stage_b is None:
+    # non-latent downscale - latent downscale cause bad quality
+    try:
+        # try to decode image normally
         refined_image = vae.decode(refined_latent['samples'])
-    else:
-        refined_image = stage_b
+    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, )
 
     if detailer_hook is not None:
         refined_image = detailer_hook.post_decode(refined_image)
@@ -312,11 +396,14 @@ def enhance_detail_for_animatediff(image_frames, model, clip, vae, guide_size, g
                                    wildcard_opt=None, wildcard_opt_concat_mode=None,
                                    detailer_hook=None,
                                    refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None,
-                                   refiner_negative=None, control_net_wrapper=None, inpaint_model=False, noise_mask_feather=0):
+                                   refiner_negative=None, control_net_wrapper=None, noise_mask_feather=0, scheduler_func=None):
     if noise_mask is not None:
         noise_mask = utils.tensor_gaussian_blur_mask(noise_mask, noise_mask_feather)
         noise_mask = noise_mask.squeeze(3)
 
+    if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
+        model = nodes_differential_diffusion.DifferentialDiffusion().apply(model)[0]
+
     if wildcard_opt is not None and wildcard_opt != "":
         model, _, wildcard_positive = wildcards.process_with_loras(wildcard_opt, model, clip)
 
@@ -423,7 +510,7 @@ def enhance_detail_for_animatediff(image_frames, model, clip, vae, guide_size, g
         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)
+                                                      latent, denoise, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative, scheduler_func=scheduler_func)
 
     if detailer_hook is not None:
         refined_latent = detailer_hook.pre_decode(refined_latent)
@@ -489,16 +576,61 @@ def sam_predict(predictor, points, plabs, bbox, threshold):
     return total_masks
 
 
-def make_sam_mask(sam_model, segs, image, detection_hint, dilation,
+class SAMWrapper:
+    def __init__(self, model, is_auto_mode, safe_to_gpu=None):
+        self.model = model
+        self.safe_to_gpu = safe_to_gpu if safe_to_gpu is not None else SafeToGPU_stub()
+        self.is_auto_mode = is_auto_mode
+
+    def prepare_device(self):
+        if self.is_auto_mode:
+            device = comfy.model_management.get_torch_device()
+            self.safe_to_gpu.to_device(self.model, device=device)
+
+    def release_device(self):
+        if self.is_auto_mode:
+            self.model.to(device="cpu")
+
+    def predict(self, image, points, plabs, bbox, threshold):
+        predictor = SamPredictor(self.model)
+        predictor.set_image(image, "RGB")
+
+        return sam_predict(predictor, points, plabs, bbox, threshold)
+
+
+class ESAMWrapper:
+    def __init__(self, model, device):
+        self.model = model
+        self.func_inference = nodes.NODE_CLASS_MAPPINGS['Yoloworld_ESAM_Zho']
+        self.device = device
+
+    def prepare_device(self):
+        pass
+
+    def release_device(self):
+        pass
+
+    def predict(self, image, points, plabs, bbox, threshold):
+        if self.device == 'CPU':
+            self.device = 'cpu'
+        else:
+            self.device = 'cuda'
+
+        detected_masks = self.func_inference.inference_sam_with_boxes(image=image, xyxy=[bbox], model=self.model, device=self.device)
+        return [detected_masks.squeeze(0)]
+
+
+def make_sam_mask(sam, segs, image, detection_hint, dilation,
                   threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
-    if sam_model.is_auto_mode:
-        device = comfy.model_management.get_torch_device()
-        sam_model.safe_to.to_device(sam_model, device=device)
+
+    if not hasattr(sam, 'sam_wrapper'):
+        raise Exception("[Impact Pack] Invalid SAMLoader is connected. Make sure 'SAMLoader (Impact)'.\nKnown issue: The ComfyUI-YOLO node overrides the SAMLoader (Impact), making it unusable. You need to uninstall ComfyUI-YOLO.\n\n\n")
+
+    sam_obj = sam.sam_wrapper
+    sam_obj.prepare_device()
 
     try:
-        predictor = SamPredictor(sam_model)
         image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
-        predictor.set_image(image, "RGB")
 
         total_masks = []
 
@@ -521,7 +653,7 @@ def make_sam_mask(sam_model, segs, image, detection_hint, dilation,
                 else:
                     plabs.append(1)
 
-            detected_masks = sam_predict(predictor, points, plabs, None, threshold)
+            detected_masks = sam_obj.predict(image, points, plabs, None, threshold)
             total_masks += detected_masks
 
         else:
@@ -590,15 +722,14 @@ def make_sam_mask(sam_model, segs, image, detection_hint, dilation,
                     points += npoints
                     plabs += nplabs
 
-                detected_masks = sam_predict(predictor, points, plabs, dilated_bbox, threshold)
+                detected_masks = sam_obj.predict(image, points, plabs, dilated_bbox, threshold)
                 total_masks += detected_masks
 
         # merge every collected masks
         mask = combine_masks2(total_masks)
 
     finally:
-        if sam_model.is_auto_mode:
-            sam_model.to(device="cpu")
+        sam_obj.release_device()
 
     if mask is not None:
         mask = mask.float()
@@ -732,9 +863,15 @@ def segs_scale_match(segs, target_shape):
         new_w = crop_region[2] - crop_region[0]
         new_h = crop_region[3] - crop_region[1]
 
-        cropped_mask = torch.from_numpy(cropped_mask)
-        cropped_mask = torch.nn.functional.interpolate(cropped_mask.unsqueeze(0).unsqueeze(0), size=(new_h, new_w), mode='bilinear', align_corners=False)
-        cropped_mask = cropped_mask.squeeze(0).squeeze(0).numpy()
+        if isinstance(cropped_mask, np.ndarray):
+            cropped_mask = torch.from_numpy(cropped_mask)
+
+        if isinstance(cropped_mask, torch.Tensor) and len(cropped_mask.shape) == 3:
+            cropped_mask = torch.nn.functional.interpolate(cropped_mask.unsqueeze(0), size=(new_h, new_w), mode='bilinear', align_corners=False)
+            cropped_mask = cropped_mask.squeeze(0)
+        else:
+            cropped_mask = torch.nn.functional.interpolate(cropped_mask.unsqueeze(0).unsqueeze(0), size=(new_h, new_w), mode='bilinear', align_corners=False)
+            cropped_mask = cropped_mask.squeeze(0).squeeze(0).numpy()
 
         if cropped_image is not None:
             cropped_image = tensor_resize(cropped_image if isinstance(cropped_image, torch.Tensor) else torch.from_numpy(cropped_image), new_w, new_h)
@@ -753,16 +890,17 @@ def every_three_pick_last(stacked_masks):
     return selected_masks
 
 
-def make_sam_mask_segmented(sam_model, segs, image, detection_hint, dilation,
+def make_sam_mask_segmented(sam, segs, image, detection_hint, dilation,
                             threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
-    if sam_model.is_auto_mode:
-        device = comfy.model_management.get_torch_device()
-        sam_model.safe_to.to_device(sam_model, device=device)
+
+    if not hasattr(sam, 'sam_wrapper'):
+        raise Exception("[Impact Pack] Invalid SAMLoader is connected. Make sure 'SAMLoader (Impact)'.")
+
+    sam_obj = sam.sam_wrapper
+    sam_obj.prepare_device()
 
     try:
-        predictor = SamPredictor(sam_model)
         image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
-        predictor.set_image(image, "RGB")
 
         total_masks = []
 
@@ -785,7 +923,7 @@ def make_sam_mask_segmented(sam_model, segs, image, detection_hint, dilation,
                 else:
                     plabs.append(1)
 
-            detected_masks = sam_predict(predictor, points, plabs, None, threshold)
+            detected_masks = sam_obj.predict(image, points, plabs, None, threshold)
             total_masks += detected_masks
 
         else:
@@ -803,7 +941,7 @@ def make_sam_mask_segmented(sam_model, segs, image, detection_hint, dilation,
                                                          mask_hint_threshold, use_small_negative,
                                                          mask_hint_use_negative)
 
-                detected_masks = sam_predict(predictor, points, plabs, dilated_bbox, threshold)
+                detected_masks = sam_obj.predict(image, points, plabs, dilated_bbox, threshold)
 
                 total_masks += detected_masks
 
@@ -811,10 +949,7 @@ def make_sam_mask_segmented(sam_model, segs, image, detection_hint, dilation,
         mask = combine_masks2(total_masks)
 
     finally:
-        if sam_model.is_auto_mode:
-            sam_model.cpu()
-
-        pass
+        sam_obj.release_device()
 
     mask_working_device = torch.device("cpu")
 
@@ -862,6 +997,32 @@ def segs_bitwise_and_mask(segs, mask):
     return segs[0], items
 
 
+def segs_bitwise_subtract_mask(segs, mask):
+    mask = make_2d_mask(mask)
+
+    if mask is None:
+        print("[SegsBitwiseSubtractMask] Cannot operate: MASK is empty.")
+        return ([],)
+
+    items = []
+
+    mask = (mask.cpu().numpy() * 255).astype(np.uint8)
+
+    for seg in segs[1]:
+        cropped_mask = (seg.cropped_mask * 255).astype(np.uint8)
+        crop_region = seg.crop_region
+
+        cropped_mask2 = mask[crop_region[1]:crop_region[3], crop_region[0]:crop_region[2]]
+
+        new_mask = cv2.subtract(cropped_mask.astype(np.uint8), cropped_mask2)
+        new_mask = new_mask.astype(np.float32) / 255.0
+
+        item = SEG(seg.cropped_image, new_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None)
+        items.append(item)
+
+    return segs[0], items
+
+
 def apply_mask_to_each_seg(segs, masks):
     if masks is None:
         print("[SegsBitwiseAndMask] Cannot operate: MASK is empty.")
@@ -959,6 +1120,21 @@ class ONNXDetector:
         pass
 
 
+def batch_mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size=1, label='A', crop_min_size=None, detailer_hook=None):
+    combined_mask = mask.max(dim=0).values
+
+    segs = mask_to_segs(combined_mask, combined, crop_factor, bbox_fill, drop_size, label, crop_min_size, detailer_hook)
+
+    new_segs = []
+    for seg in segs[1]:
+        x1, y1, x2, y2 = seg.crop_region
+        cropped_mask = mask[:, y1:y2, x1:x2]
+        item = SEG(None, cropped_mask, 1.0, seg.crop_region, seg.bbox, label, None)
+        new_segs.append(item)
+
+    return segs[0], new_segs
+
+
 def mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size=1, label='A', crop_min_size=None, detailer_hook=None, is_contour=True):
     drop_size = max(drop_size, 1)
     if mask is None:
@@ -1058,7 +1234,7 @@ def mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size=1, label='A',
                         cropped_mask[by1:by2, bx1:bx2] = 1.0
 
                     if cropped_mask is not None:
-                        cropped_mask = utils.to_binary_mask(torch.from_numpy(cropped_mask), 0.1)[0]
+                        cropped_mask = torch.clip(torch.from_numpy(cropped_mask), 0, 1.0)
                         item = SEG(None, cropped_mask.numpy(), 1.0, crop_region, bbox, label, None)
                         result.append(item)
 
@@ -1191,9 +1367,14 @@ def segs_to_masklist(segs):
     return masks
 
 
-def vae_decode(vae, samples, use_tile, hook, tile_size=512):
+def vae_decode(vae, samples, use_tile, hook, tile_size=512, overlap=64):
     if use_tile:
-        pixels = nodes.VAEDecodeTiled().decode(vae, samples, tile_size)[0]
+        decoder = nodes.VAEDecodeTiled()
+        if 'overlap' in inspect.signature(decoder.decode).parameters:
+            pixels = decoder.decode(vae, samples, tile_size, overlap=overlap)[0]
+        else:
+            print(f"[Impact Pack] Your ComfyUI is outdated.")
+            pixels = decoder.decode(vae, samples, tile_size)[0]
     else:
         pixels = nodes.VAEDecode().decode(vae, samples)[0]
 
@@ -1215,24 +1396,31 @@ def vae_encode(vae, pixels, use_tile, hook, tile_size=512):
     return samples
 
 
-def latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae, use_tile=False, tile_size=512,
-                                        save_temp_prefix=None, hook=None):
-    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size)
+def latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    return latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0]
+
+
+def latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap)
 
     if save_temp_prefix is not None:
         nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
 
     pixels = nodes.ImageScale().upscale(pixels, scale_method, int(w), int(h), False)[0]
 
+    old_pixels = pixels
     if hook is not None:
         pixels = hook.post_upscale(pixels)
 
-    return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size)
+    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
 
 
-def latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512,
-                                  save_temp_prefix=None, hook=None):
-    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size)
+def latent_upscale_on_pixel_space(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    return latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0]
+
+
+def latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap)
 
     if save_temp_prefix is not None:
         nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
@@ -1241,20 +1429,19 @@ def latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use
     h = pixels.shape[1] * scale_factor
     pixels = nodes.ImageScale().upscale(pixels, scale_method, int(w), int(h), False)[0]
 
+    old_pixels = pixels
     if hook is not None:
         pixels = hook.post_upscale(pixels)
 
-    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), pixels)
+    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
 
 
-def latent_upscale_on_pixel_space(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512,
-                                  save_temp_prefix=None, hook=None):
-    return latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook)[0]
+def latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    return latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0]
 
 
-def latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, upscale_model, new_w, new_h, vae,
-                                                   use_tile=False, tile_size=512, save_temp_prefix=None, hook=None):
-    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size)
+def latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap)
 
     if save_temp_prefix is not None:
         nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
@@ -1273,15 +1460,20 @@ def latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, upscal
     # downscale to target scale
     pixels = nodes.ImageScale().upscale(pixels, scale_method, int(new_w), int(new_h), False)[0]
 
+    old_pixels = pixels
     if hook is not None:
         pixels = hook.post_upscale(pixels)
 
-    return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size)
+    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
 
 
+def latent_upscale_on_pixel_space_with_model(samples, scale_method, upscale_model, scale_factor, vae, use_tile=False,
+                                             tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    return latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_model, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0]
+
 def latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_model, scale_factor, vae, use_tile=False,
-                                              tile_size=512, save_temp_prefix=None, hook=None):
-    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size)
+                                              tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap)
 
     if save_temp_prefix is not None:
         nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
@@ -1304,14 +1496,11 @@ def latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_mod
     # downscale to target scale
     pixels = nodes.ImageScale().upscale(pixels, scale_method, int(new_w), int(new_h), False)[0]
 
+    old_pixels = pixels
     if hook is not None:
         pixels = hook.post_upscale(pixels)
 
-    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), pixels)
-
-def latent_upscale_on_pixel_space_with_model(samples, scale_method, upscale_model, scale_factor, vae, use_tile=False,
-                                             tile_size=512, save_temp_prefix=None, hook=None):
-    return latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_model, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook)[0]
+    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
 
 
 class TwoSamplersForMaskUpscaler:
@@ -1332,6 +1521,7 @@ class TwoSamplersForMaskUpscaler:
         self.hook_full = hook_full_opt
         self.use_tiled_vae = use_tiled_vae
         self.tile_size = tile_size
+        self.is_tiled = False
         self.vae = vae
 
     def upscale(self, step_info, samples, upscale_factor, save_temp_prefix=None):
@@ -1456,7 +1646,8 @@ class TwoSamplersForMaskUpscaler:
 
 class PixelKSampleUpscaler:
     def __init__(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise,
-                 use_tiled_vae, upscale_model_opt=None, hook_opt=None, tile_size=512):
+                 use_tiled_vae, upscale_model_opt=None, hook_opt=None, tile_size=512, scheduler_func=None,
+                 tile_cnet_opt=None, tile_cnet_strength=1.0):
         self.params = scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
         self.upscale_model = upscale_model_opt
         self.hook = hook_opt
@@ -1464,6 +1655,28 @@ class PixelKSampleUpscaler:
         self.tile_size = tile_size
         self.is_tiled = False
         self.vae = vae
+        self.scheduler_func = scheduler_func
+        self.tile_cnet = tile_cnet_opt
+        self.tile_cnet_strength = tile_cnet_strength
+
+    def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise, images):
+        if self.tile_cnet is not None:
+            image_batch, image_w, image_h, _ = images.shape
+            if image_batch > 1:
+                warnings.warn('Multiple latents in batch, Tile ControlNet being ignored')
+            else:
+                if 'TilePreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
+                    raise RuntimeError("'TilePreprocessor' node (from comfyui_controlnet_aux) isn't installed.")
+                preprocessor = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']()
+                # might add capacity to set pyrUp_iters later, not needed for now though
+                preprocessed = preprocessor.execute(images, pyrUp_iters=3, resolution=min(image_w, image_h))[0]
+                apply_cnet = getattr(nodes.ControlNetApply(), nodes.ControlNetApply.FUNCTION)
+                positive = apply_cnet(positive, self.tile_cnet, preprocessed, strength=self.tile_cnet_strength)[0]
+
+        refined_latent = impact_sampling.impact_sample(model, seed, steps, cfg, sampler_name, scheduler,
+                                                       positive, negative, upscaled_latent, denoise, scheduler_func=self.scheduler_func)
+
+        return refined_latent
 
     def upscale(self, step_info, samples, upscale_factor, save_temp_prefix=None):
         scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
@@ -1472,24 +1685,28 @@ class PixelKSampleUpscaler:
             self.hook.set_steps(step_info)
 
         if self.upscale_model is None:
-            upscaled_latent = latent_upscale_on_pixel_space(samples, scale_method, upscale_factor, vae,
-                                                            use_tile=self.use_tiled_vae,
-                                                            save_temp_prefix=save_temp_prefix, hook=self.hook)
+            upscaled_latent, upscaled_images = \
+                latent_upscale_on_pixel_space2(samples, scale_method, upscale_factor, vae,
+                                               use_tile=self.use_tiled_vae,
+                                               save_temp_prefix=save_temp_prefix, hook=self.hook, tile_size=512)
         else:
-            upscaled_latent = latent_upscale_on_pixel_space_with_model(samples, scale_method, self.upscale_model,
-                                                                       upscale_factor, vae,
-                                                                       use_tile=self.use_tiled_vae,
-                                                                       save_temp_prefix=save_temp_prefix,
-                                                                       hook=self.hook,
-                                                                       tile_size=self.tile_size)
+            upscaled_latent, upscaled_images = \
+                latent_upscale_on_pixel_space_with_model2(samples, scale_method, self.upscale_model,
+                                                          upscale_factor, vae,
+                                                          use_tile=self.use_tiled_vae,
+                                                          save_temp_prefix=save_temp_prefix,
+                                                          hook=self.hook,
+                                                          tile_size=self.tile_size)
 
         if self.hook is not None:
             model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \
                 self.hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
                                       upscaled_latent, denoise)
 
-        refined_latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler,
-                                                 positive, negative, upscaled_latent, denoise)[0]
+        if 'noise_mask' in samples:
+            upscaled_latent['noise_mask'] = samples['noise_mask']
+
+        refined_latent = self.sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise, upscaled_images)
         return refined_latent
 
     def upscale_shape(self, step_info, samples, w, h, save_temp_prefix=None):
@@ -1499,30 +1716,34 @@ class PixelKSampleUpscaler:
             self.hook.set_steps(step_info)
 
         if self.upscale_model is None:
-            upscaled_latent = latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae,
-                                                                  use_tile=self.use_tiled_vae,
-                                                                  save_temp_prefix=save_temp_prefix, hook=self.hook,
-                                                                  tile_size=self.tile_size)
+            upscaled_latent, upscaled_images = \
+                latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae,
+                                                     use_tile=self.use_tiled_vae,
+                                                     save_temp_prefix=save_temp_prefix, hook=self.hook,
+                                                     tile_size=self.tile_size)
         else:
-            upscaled_latent = latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, self.upscale_model,
-                                                                             w, h, vae,
-                                                                             use_tile=self.use_tiled_vae,
-                                                                             save_temp_prefix=save_temp_prefix,
-                                                                             hook=self.hook,
-                                                                             tile_size=self.tile_size)
+            upscaled_latent, upscaled_images = \
+                latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, self.upscale_model,
+                                                                w, h, vae,
+                                                                use_tile=self.use_tiled_vae,
+                                                                save_temp_prefix=save_temp_prefix,
+                                                                hook=self.hook,
+                                                                tile_size=self.tile_size)
 
         if self.hook is not None:
             model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \
                 self.hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
                                       upscaled_latent, denoise)
 
-        refined_latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler,
-                                                 positive, negative, upscaled_latent, denoise)[0]
+        if 'noise_mask' in samples:
+            upscaled_latent['noise_mask'] = samples['noise_mask']
+
+        refined_latent = self.sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise, upscaled_images)
         return refined_latent
 
 
 class IPAdapterWrapper:
-    def __init__(self, ipadapter_pipe, weight, noise, weight_type, start_at, end_at, unfold_batch, faceid_v2, weight_v2, reference_image, prev_control_net=None):
+    def __init__(self, ipadapter_pipe, weight, noise, weight_type, start_at, end_at, unfold_batch, weight_v2, reference_image, neg_image=None, prev_control_net=None, combine_embeds='concat'):
         self.reference_image = reference_image
         self.ipadapter_pipe = ipadapter_pipe
         self.weight = weight
@@ -1532,21 +1753,25 @@ class IPAdapterWrapper:
         self.end_at = end_at
         self.unfold_batch = unfold_batch
         self.prev_control_net = prev_control_net
-        self.faceid_v2 = faceid_v2
         self.weight_v2 = weight_v2
         self.image = reference_image
+        self.neg_image = neg_image
+        self.combine_embeds = combine_embeds
 
     # name 'apply_ipadapter' isn't allowed
     def doit_ipadapter(self, model):
         cnet_image_list = [self.image]
         prev_cnet_images = []
 
-        if 'IPAdapterApply' not in nodes.NODE_CLASS_MAPPINGS:
+        if 'IPAdapterAdvanced' not in nodes.NODE_CLASS_MAPPINGS:
+            if 'IPAdapterApply' in nodes.NODE_CLASS_MAPPINGS:
+                raise Exception(f"[ERROR] 'ComfyUI IPAdapter Plus' is outdated.")
+
             utils.try_install_custom_node('https://github.com/cubiq/ComfyUI_IPAdapter_plus',
                                           "To use 'IPAdapterApplySEGS' node, 'ComfyUI IPAdapter Plus' extension is required.")
             raise Exception(f"[ERROR] To use IPAdapterApplySEGS, you need to install 'ComfyUI IPAdapter Plus'")
 
-        obj = nodes.NODE_CLASS_MAPPINGS['IPAdapterApply']
+        obj = nodes.NODE_CLASS_MAPPINGS['IPAdapterAdvanced']
 
         ipadapter, _, clip_vision, insightface, lora_loader = self.ipadapter_pipe
         model = lora_loader(model)
@@ -1554,10 +1779,10 @@ class IPAdapterWrapper:
         if self.prev_control_net is not None:
             model, prev_cnet_images = self.prev_control_net.doit_ipadapter(model)
 
-        model = obj().apply_ipadapter(ipadapter, model, self.weight, clip_vision=clip_vision, image=self.image,
-                                      embeds=None, weight_type=self.weight_type, noise=self.noise,
-                                      attn_mask=None, start_at=self.start_at, end_at=self.end_at,
-                                      unfold_batch=self.unfold_batch, insightface=insightface, faceid_v2=self.faceid_v2, weight_v2=self.weight_v2)[0]
+        model = obj().apply_ipadapter(model=model, ipadapter=ipadapter, weight=self.weight, weight_type=self.weight_type,
+                                      start_at=self.start_at, end_at=self.end_at, combine_embeds=self.combine_embeds,
+                                      clip_vision=clip_vision, image=self.image, image_negative=self.neg_image, attn_mask=None,
+                                      insightface=insightface, weight_faceidv2=self.weight_v2)[0]
 
         cnet_image_list.extend(prev_cnet_images)
 
@@ -1623,13 +1848,14 @@ class ControlNetWrapper:
 
 class ControlNetAdvancedWrapper:
     def __init__(self, control_net, strength, start_percent, end_percent, preprocessor, prev_control_net=None,
-                 original_size=None, crop_region=None, control_image=None):
+                 original_size=None, crop_region=None, control_image=None, vae=None):
         self.control_net = control_net
         self.strength = strength
         self.preprocessor = preprocessor
         self.prev_control_net = prev_control_net
         self.start_percent = start_percent
         self.end_percent = end_percent
+        self.vae = vae
 
         if original_size is not None and crop_region is not None and control_image is not None:
             self.control_image = utils.tensor_resize(control_image, original_size[1], original_size[0])
@@ -1637,6 +1863,12 @@ class ControlNetAdvancedWrapper:
         else:
             self.control_image = None
 
+    def doit_ipadapter(self, model):
+        if self.prev_control_net is not None:
+            return self.prev_control_net.doit_ipadapter(model)
+        else:
+            return model, []
+
     def apply(self, positive, negative, image, mask=None, use_acn=False):
         cnet_image_list = []
         prev_cnet_images = []
@@ -1664,7 +1896,17 @@ class ControlNetAdvancedWrapper:
                                               "To use 'ControlNetAdvancedWrapper' for AnimateDiff, 'ComfyUI-Advanced-ControlNet' extension is required.")
                 raise Exception("'ACN_AdvancedControlNetApply' node isn't installed.")
         else:
-            positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive, negative, self.control_net, cnet_image, self.strength, self.start_percent, self.end_percent)
+            if self.vae is not None:
+                apply_controlnet = nodes.ControlNetApplyAdvanced().apply_controlnet
+                signature = inspect.signature(apply_controlnet)
+
+                if 'vae' in signature.parameters:
+                    positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive, negative, self.control_net, cnet_image, self.strength, self.start_percent, self.end_percent, vae=self.vae)
+                else:
+                    print(f"[Impact Pack] ERROR: The ComfyUI version is outdated. VAE cannot be used in ApplyControlNet.")
+                    raise Exception("[Impact Pack] ERROR: The ComfyUI version is outdated. VAE cannot be used in ApplyControlNet.")
+            else:
+                positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive, negative, self.control_net, cnet_image, self.strength, self.start_percent, self.end_percent)
 
         return positive, negative, cnet_image_list
 
@@ -1700,26 +1942,41 @@ class PixelTiledKSampleUpscaler:
     def __init__(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative,
                  denoise,
                  tile_width, tile_height, tiling_strategy,
-                 upscale_model_opt=None, hook_opt=None, tile_size=512):
+                 upscale_model_opt=None, hook_opt=None, tile_cnet_opt=None, tile_size=512, tile_cnet_strength=1.0):
         self.params = scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
         self.vae = vae
         self.tile_params = tile_width, tile_height, tiling_strategy
         self.upscale_model = upscale_model_opt
         self.hook = hook_opt
+        self.tile_cnet = tile_cnet_opt
         self.tile_size = tile_size
         self.is_tiled = True
+        self.tile_cnet_strength = tile_cnet_strength
 
-    def tiled_ksample(self, latent):
+    def tiled_ksample(self, latent, images):
         if "BNK_TiledKSampler" in nodes.NODE_CLASS_MAPPINGS:
             TiledKSampler = nodes.NODE_CLASS_MAPPINGS['BNK_TiledKSampler']
         else:
             utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_TiledKSampler',
                                           "To use 'PixelTiledKSampleUpscalerProvider', 'Tiled sampling for ComfyUI' extension is required.")
-            raise Exception("'BNK_TiledKSampler' node isn't installed.")
+            raise RuntimeError("'BNK_TiledKSampler' node isn't installed.")
 
         scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
         tile_width, tile_height, tiling_strategy = self.tile_params
 
+        if self.tile_cnet is not None:
+            image_batch, image_w, image_h, _ = images.shape
+            if image_batch > 1:
+                warnings.warn('Multiple latents in batch, Tile ControlNet being ignored')
+            else:
+                if 'TilePreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
+                    raise RuntimeError("'TilePreprocessor' node (from comfyui_controlnet_aux) isn't installed.")
+                preprocessor = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']()
+                # might add capacity to set pyrUp_iters later, not needed for now though
+                preprocessed = preprocessor.execute(images, pyrUp_iters=3, resolution=min(image_w, image_h))[0]
+                apply_cnet = getattr(nodes.ControlNetApply(), nodes.ControlNetApply.FUNCTION)
+                positive = apply_cnet(positive, self.tile_cnet, preprocessed, strength=self.tile_cnet_strength)[0]
+
         return TiledKSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name,
                                       scheduler, positive, negative, latent, denoise)[0]
 
@@ -1730,19 +1987,18 @@ class PixelTiledKSampleUpscaler:
             self.hook.set_steps(step_info)
 
         if self.upscale_model is None:
-            upscaled_latent = latent_upscale_on_pixel_space(samples, scale_method, upscale_factor, vae,
-                                                            use_tile=True, save_temp_prefix=save_temp_prefix,
-                                                            hook=self.hook,
-                                                            tile_size=self.tile_size)
+            upscaled_latent, upscaled_images = \
+                latent_upscale_on_pixel_space2(samples, scale_method, upscale_factor, vae,
+                                               use_tile=True, save_temp_prefix=save_temp_prefix,
+                                               hook=self.hook, tile_size=self.tile_size)
         else:
-            upscaled_latent = latent_upscale_on_pixel_space_with_model(samples, scale_method, self.upscale_model,
-                                                                       upscale_factor, vae,
-                                                                       use_tile=True,
-                                                                       save_temp_prefix=save_temp_prefix,
-                                                                       hook=self.hook,
-                                                                       tile_size=self.tile_size)
+            upscaled_latent, upscaled_images = \
+                latent_upscale_on_pixel_space_with_model2(samples, scale_method, self.upscale_model,
+                                                          upscale_factor, vae, use_tile=True,
+                                                          save_temp_prefix=save_temp_prefix,
+                                                          hook=self.hook, tile_size=self.tile_size)
 
-        refined_latent = self.tiled_ksample(upscaled_latent)
+        refined_latent = self.tiled_ksample(upscaled_latent, upscaled_images)
 
         return refined_latent
 
@@ -1753,18 +2009,20 @@ class PixelTiledKSampleUpscaler:
             self.hook.set_steps(step_info)
 
         if self.upscale_model is None:
-            upscaled_latent = latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae,
-                                                                  use_tile=True, save_temp_prefix=save_temp_prefix,
-                                                                  hook=self.hook, tile_size=self.tile_size)
+            upscaled_latent, upscaled_images = \
+                latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae,
+                                                     use_tile=True, save_temp_prefix=save_temp_prefix,
+                                                     hook=self.hook, tile_size=self.tile_size)
         else:
-            upscaled_latent = latent_upscale_on_pixel_space_with_model_shape(samples, scale_method,
-                                                                             self.upscale_model, w, h, vae,
-                                                                             use_tile=True,
-                                                                             save_temp_prefix=save_temp_prefix,
-                                                                             hook=self.hook,
-                                                                             tile_size=self.tile_size)
+            upscaled_latent, upscaled_images = \
+                latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method,
+                                                                self.upscale_model, w, h, vae,
+                                                                use_tile=True,
+                                                                save_temp_prefix=save_temp_prefix,
+                                                                hook=self.hook,
+                                                                tile_size=self.tile_size)
 
-        refined_latent = self.tiled_ksample(upscaled_latent)
+        refined_latent = self.tiled_ksample(upscaled_latent, upscaled_images)
 
         return refined_latent
 
@@ -1839,7 +2097,7 @@ def random_mask_raw(mask, bbox, factor):
     w = x2 - x1
     h = y2 - y1
 
-    factor = int(min(w, h) * factor / 4)
+    factor = max(6, int(min(w, h) * factor / 4))
 
     def draw_random_circle(center, radius):
         i, j = center
@@ -1899,6 +2157,12 @@ def adaptive_mask_paste(dest_mask, src_mask, bbox):
     dest_mask[y1:y2, x1:x2] = bbox_mask
 
 
+def crop_condition_mask(mask, image, crop_region):
+    cond_scale = (mask.shape[1] / image.shape[1], mask.shape[2] / image.shape[2])
+    mask_region = [round(v * cond_scale[i % 2]) for i, v in enumerate(crop_region)]
+    return crop_ndarray3(mask, mask_region)
+
+
 class SafeToGPU:
     def __init__(self, size):
         self.size = size
@@ -1934,7 +2198,10 @@ try:
 
         if method != LatentPreviewMethod.NoPreviews or force:
             # TODO previewer methods
-            taesd_decoder_path = folder_paths.get_full_path("vae_approx", latent_format.taesd_decoder_name)
+            taesd_decoder_path = None
+
+            if hasattr(latent_format, "taesd_decoder_path"):
+                taesd_decoder_path = folder_paths.get_full_path("vae_approx", latent_format.taesd_decoder_name)
 
             if method == LatentPreviewMethod.Auto:
                 method = LatentPreviewMethod.Latent2RGB
@@ -1943,7 +2210,7 @@ try:
 
             if method == LatentPreviewMethod.TAESD:
                 if taesd_decoder_path:
-                    taesd = TAESD(None, taesd_decoder_path).to(device)
+                    taesd = TAESD(None, taesd_decoder_path, latent_channels=latent_format.latent_channels).to(device)
                     previewer = TAESDPreviewerImpl(taesd)
                 else:
                     print("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(

modules/impact/detectors.py

@@ -1,25 +1,35 @@
 import impact.core as core
-from impact.config import MAX_RESOLUTION
+from nodes import MAX_RESOLUTION
 import impact.segs_nodes as segs_nodes
 import impact.utils as utils
 import torch
 from impact.core import SEG
 
+SAM_MODEL_TOOLTIP = {"tooltip": "Segment Anything Model for Silhouette Detection.\nBe sure to use the SAM_MODEL loaded through the SAMLoader (Impact) node as input."}
+SAM_MODEL_TOOLTIP_OPTIONAL = {"tooltip": "[OPTIONAL]\nSegment Anything Model for Silhouette Detection.\nBe sure to use the SAM_MODEL loaded through the SAMLoader (Impact) node as input.\nGiven this input, it refines the rectangular areas detected by BBOX_DETECTOR into silhouette shapes through SAM.\nsam_model_opt takes priority over segm_detector_opt."}
+
+MASK_HINT_THRESHOLD_TOOLTIP = "When detection_hint is mask-area, the mask of SEGS is used as a point hint for SAM (Segment Anything).\nIn this case, only the areas of the mask with brightness values equal to or greater than mask_hint_threshold are used as hints."
+MASK_HINT_USE_NEGATIVE_TOOLTIP = "When detecting with SAM (Segment Anything), negative hints are applied as follows:\nSmall: When the SEGS is smaller than 10 pixels in size\nOuter: Sampling the image area outside the SEGS region at regular intervals"
+
+DILATION_TOOLTIP = "Set the value to dilate the result mask. If the value is negative, it erodes the mask."
+DETECTION_HINT_TOOLTIP = {"tooltip": "It is recommended to use only center-1.\nWhen refining the mask of SEGS with the SAM (Segment Anything) model, center-1 uses only the rectangular area of SEGS and a single point at the exact center as hints.\nOther options were added during the experimental stage and do not work well."}
+
+BBOX_EXPANSION_TOOLTIP = "When performing SAM (Segment Anything) detection within the SEGS area, the rectangular area of SEGS is expanded and used as a hint."
 
 class SAMDetectorCombined:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                        "sam_model": ("SAM_MODEL", ),
-                        "segs": ("SEGS", ),
-                        "image": ("IMAGE", ),
+                        "sam_model": ("SAM_MODEL", SAM_MODEL_TOOLTIP),
+                        "segs": ("SEGS", {"tooltip": "This is the segment information detected by the detector.\nIt refines the Mask through the SAM (Segment Anything) detector for all areas pointed to by SEGS, and combines all Masks to return as a single Mask."}),
+                        "image": ("IMAGE", {"tooltip": "It is assumed that segs contains only the information about the detected areas, and does not include the image. SAM (Segment Anything) operates by referencing this image."}),
                         "detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area",
-                                            "mask-points", "mask-point-bbox", "none"],),
-                        "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
-                        "threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}),
-                        "bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
-                        "mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
-                        "mask_hint_use_negative": (["False", "Small", "Outter"], )
+                                            "mask-points", "mask-point-bbox", "none"], DETECTION_HINT_TOOLTIP),
+                        "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1, "tooltip": DILATION_TOOLTIP}),
+                        "threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Set the sensitivity threshold for the mask detected by SAM (Segment Anything). A higher value generates a more specific mask with a narrower range. For example, when pointing to a person's area, it might detect clothes, which is a narrower range, instead of the entire person."}),
+                        "bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": BBOX_EXPANSION_TOOLTIP}),
+                        "mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": MASK_HINT_THRESHOLD_TOOLTIP}),
+                        "mask_hint_use_negative": (["False", "Small", "Outter"], {"tooltip": MASK_HINT_USE_NEGATIVE_TOOLTIP})
                       }
                 }
 
@@ -38,16 +48,16 @@ class SAMDetectorSegmented:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                        "sam_model": ("SAM_MODEL", ),
-                        "segs": ("SEGS", ),
-                        "image": ("IMAGE", ),
+                        "sam_model": ("SAM_MODEL", SAM_MODEL_TOOLTIP),
+                        "segs": ("SEGS", {"tooltip": "This is the segment information detected by the detector.\nFor the SEGS region, the masks detected by SAM (Segment Anything) are created as a unified mask and a batch of individual masks."}),
+                        "image": ("IMAGE", {"tooltip": "It is assumed that segs contains only the information about the detected areas, and does not include the image. SAM (Segment Anything) operates by referencing this image."}),
                         "detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area",
-                                            "mask-points", "mask-point-bbox", "none"],),
-                        "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
+                                            "mask-points", "mask-point-bbox", "none"], DETECTION_HINT_TOOLTIP),
+                        "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1, "tooltip": DILATION_TOOLTIP}),
                         "threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}),
-                        "bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
-                        "mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
-                        "mask_hint_use_negative": (["False", "Small", "Outter"], )
+                        "bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": BBOX_EXPANSION_TOOLTIP}),
+                        "mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": MASK_HINT_THRESHOLD_TOOLTIP}),
+                        "mask_hint_use_negative": (["False", "Small", "Outter"], {"tooltip": MASK_HINT_USE_NEGATIVE_TOOLTIP})
                       }
                 }
 
@@ -151,7 +161,11 @@ class SegmDetectorCombined:
 
     def doit(self, segm_detector, image, threshold, dilation):
         mask = segm_detector.detect_combined(image, threshold, dilation)
-        return (mask,)
+
+        if mask is None:
+            mask = torch.zeros((image.shape[2], image.shape[1]), dtype=torch.float32, device="cpu")
+
+        return (mask.unsqueeze(0),)
 
 
 class BboxDetectorCombined(SegmDetectorCombined):
@@ -167,7 +181,11 @@ class BboxDetectorCombined(SegmDetectorCombined):
 
     def doit(self, bbox_detector, image, threshold, dilation):
         mask = bbox_detector.detect_combined(image, threshold, dilation)
-        return (mask,)
+
+        if mask is None:
+            mask = torch.zeros((image.shape[2], image.shape[1]), dtype=torch.float32, device="cpu")
+
+        return (mask.unsqueeze(0),)
 
 
 class SimpleDetectorForEach:
@@ -191,7 +209,7 @@ class SimpleDetectorForEach:
                       },
                 "optional": {
                         "post_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
-                        "sam_model_opt": ("SAM_MODEL", ),
+                        "sam_model_opt": ("SAM_MODEL", SAM_MODEL_TOOLTIP_OPTIONAL),
                         "segm_detector_opt": ("SEGM_DETECTOR", ),
                       }
                 }
@@ -303,7 +321,7 @@ class SimpleDetectorForAnimateDiff:
                 "optional": {
                         "masking_mode": (["Pivot SEGS", "Combine neighboring frames", "Don't combine"],),
                         "segs_pivot": (["Combined mask", "1st frame mask"],),
-                        "sam_model_opt": ("SAM_MODEL", ),
+                        "sam_model_opt": ("SAM_MODEL", SAM_MODEL_TOOLTIP_OPTIONAL),
                         "segm_detector_opt": ("SEGM_DETECTOR", ),
                  }
                 }
@@ -402,13 +420,14 @@ class SimpleDetectorForAnimateDiff:
                 return segs_by_frames[0][1]
             else:
                 merged_mask = get_whole_merged_mask()
-                return segs_nodes.MaskToSEGS().doit(merged_mask, False, crop_factor, False, drop_size, contour_fill=True)[0]
+                return segs_nodes.MaskToSEGS.doit(merged_mask, False, crop_factor, False, drop_size, contour_fill=True)[0]
 
-        def get_merged_neighboring_segs():
+        def get_segs(merged_neighboring=False):
             pivot_segs = get_pivot_segs()
 
             masks_by_frame = get_masked_frames()
-            masks_by_frame = get_merged_neighboring_mask(masks_by_frame)
+            if merged_neighboring:
+                masks_by_frame = get_merged_neighboring_mask(masks_by_frame)
 
             new_segs = []
             for seg in pivot_segs[1]:
@@ -427,33 +446,15 @@ class SimpleDetectorForAnimateDiff:
 
             return pivot_segs[0], new_segs
 
-        def get_separated_segs():
-            pivot_segs = get_pivot_segs()
-
-            masks_by_frame = get_masked_frames()
-
-            new_segs = []
-            for seg in pivot_segs[1]:
-                cropped_mask = torch.zeros(seg.cropped_mask.shape, dtype=torch.float32, device="cpu").unsqueeze(0)
-                x1, y1, x2, y2 = seg.crop_region
-                for mask in masks_by_frame:
-                    cropped_mask_at_frame = mask[y1:y2, x1:x2]
-                    cropped_mask = torch.cat((cropped_mask, cropped_mask_at_frame), dim=0)
-
-                new_seg = SEG(seg.cropped_image, cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper)
-                new_segs.append(new_seg)
-
-            return pivot_segs[0], new_segs
-
         # create result mask
         if masking_mode == "Pivot SEGS":
             return (get_pivot_segs(), )
 
         elif masking_mode == "Combine neighboring frames":
-            return (get_merged_neighboring_segs(), )
+            return (get_segs(merged_neighboring=True), )
 
         else: # elif masking_mode == "Don't combine":
-            return (get_separated_segs(), )
+            return (get_segs(merged_neighboring=False), )
 
     def doit(self, bbox_detector, image_frames, bbox_threshold, bbox_dilation, crop_factor, drop_size,
              sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold,

modules/impact/hf_nodes.py

@@ -42,7 +42,7 @@ class HF_TransformersClassifierProvider:
         else:
             device = "cpu"
 
-        classifier = pipeline(model=url, device=device)
+        classifier = pipeline('image-classification', model=url, device=device)
 
         return (classifier,)
 
@@ -83,8 +83,9 @@ class SEGS_Classify:
                     }
                 }
 
-    RETURN_TYPES = ("SEGS", "SEGS",)
-    RETURN_NAMES = ("filtered_SEGS", "remained_SEGS",)
+    RETURN_TYPES = ("SEGS", "SEGS", "STRING")
+    RETURN_NAMES = ("filtered_SEGS", "remained_SEGS", "detected_labels")
+    OUTPUT_IS_LIST = (False, False, True)
 
     FUNCTION = "doit"
 
@@ -117,7 +118,7 @@ class SEGS_Classify:
         match = re.match(classify_expr_pattern, expr_str)
 
         if match is None:
-            return ((segs[0], []), segs)
+            return (segs[0], []), segs, []
 
         a = match.group(1)
         op = match.group(2)
@@ -128,6 +129,7 @@ class SEGS_Classify:
 
         classified = []
         remained_SEGS = []
+        provided_labels = set()
 
         for seg in segs[1]:
             cropped_image = None
@@ -142,6 +144,9 @@ class SEGS_Classify:
                 cropped_image = to_pil(cropped_image)
                 res = classifier(cropped_image)
                 classified.append((seg, res))
+
+                for x in res:
+                    provided_labels.add(x['label'])
             else:
                 remained_SEGS.append(seg)
 
@@ -180,4 +185,4 @@ class SEGS_Classify:
             else:
                 remained_SEGS.append(seg)
 
-        return ((segs[0], filtered_SEGS), (segs[0], remained_SEGS))
+        return (segs[0], filtered_SEGS), (segs[0], remained_SEGS), list(provided_labels)

modules/impact/hook_nodes.py

@@ -1,7 +1,6 @@
 import sys
 from . import hooks
 from . import defs
-import comfy
 
 
 class SEGSOrderedFilterDetailerHookProvider:
@@ -74,38 +73,13 @@ class PreviewDetailerHookProvider:
             "hidden": {"unique_id": "UNIQUE_ID"},
         }
 
-    RETURN_TYPES = ("DETAILER_HOOK", )
+    RETURN_TYPES = ("DETAILER_HOOK", "UPSCALER_HOOK")
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Util"
 
+    NOT_IDEMPOTENT = True
+
     def doit(self, quality, unique_id):
         hook = hooks.PreviewDetailerHook(unique_id, quality)
-        return (hook, )
-
-
-class StableCascade_DetailerHookProvider:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "b_model": ("MODEL",),
-                "b_vae": ("VAE",),
-                "b_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                "b_steps": ("INT", {"default": 5, "min": 1, "max": 10000}),
-                "b_cfg": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
-                "b_sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
-                "b_scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
-                "c_compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
-            },
-        }
-
-    RETURN_TYPES = ("DETAILER_HOOK", )
-    FUNCTION = "doit"
-
-    CATEGORY = "ImpactPack/Util"
-
-    def doit(self, b_model, b_vae, b_seed, b_steps, b_cfg, b_sampler_name, b_scheduler, c_compression):
-        hook = hooks.StableCascade_DetailerHook(b_model, b_vae, b_seed, b_steps, b_cfg, b_sampler_name, b_scheduler, c_compression)
-        return (hook, )
-
+        return hook, hook

modules/impact/hooks.py

@@ -1,8 +1,6 @@
 import copy
-
-import comfy_extras.nodes_stable_cascade
+import torch
 import nodes
-
 from impact import utils
 from . import segs_nodes
 from thirdparty import noise_nodes
@@ -10,7 +8,8 @@ from server import PromptServer
 import asyncio
 import folder_paths
 import os
-from impact import impact_sampling
+from comfy_extras import nodes_custom_sampler
+import math
 
 
 class PixelKSampleHook:
@@ -105,19 +104,10 @@ class DetailerHookCombine(PixelKSampleHookCombine):
         image = self.hook2.post_paste(image)
         return image
 
-    def stable_cascade_vae_encode(self, vae, pixels):
-        latent = self.hook1.stable_cascade_vae_encode(vae, pixels)
-        if latent is not None:
-            return latent
-
-        return self.hook2.stable_cascade_vae_encode(vae, pixels)
-
-    def stable_cascade_stage_b(self, image, positive, negative, latent):
-        image = self.hook1.stable_cascade_stage_b(image, positive, negative, latent)
-        if image is not None:
-            return image
-
-        return self.hook2.stable_cascade_stage_b(image, positive, negative, latent)
+    def get_custom_noise(self, seed, noise, is_touched):
+        noise_1st, is_touched = self.hook1.get_custom_noise(seed, noise, is_touched)
+        noise_2nd, is_touched = self.hook2.get_custom_noise(seed, noise, is_touched)
+        return noise, is_touched
 
 
 class SimpleCfgScheduleHook(PixelKSampleHook):
@@ -180,43 +170,38 @@ class DetailerHook(PixelKSampleHook):
     def post_paste(self, image):
         return image
 
-    def stable_cascade_vae_encode(self, vae, pixels):
-        return None
-
-    def stable_cascade_stage_b(self, image, positive, negative, latent):
-        return None
+    def get_custom_noise(self, seed, noise, is_touched):
+        return noise, is_touched
 
 
-class StableCascade_DetailerHook(DetailerHook):
-    def __init__(self, b_model, b_vae, b_seed, b_steps, b_cfg, b_sampler_name, b_scheduler, c_compression):
+# class CustomNoiseDetailerHookProvider(DetailerHook):
+#     def __init__(self, noise):
+#         super().__init__()
+#         self.noise = noise
+#
+#     def get_custom_noise(self, seed, noise, is_start):
+#         return self.noise
+
+
+class VariationNoiseDetailerHookProvider(DetailerHook):
+    def __init__(self, variation_seed, variation_strength):
         super().__init__()
-        self.b_model = b_model
-        self.b_vae = b_vae
-        self.b_seed = b_seed
-        self.b_steps = b_steps
-        self.b_cfg = b_cfg
-        self.b_sampler_name = b_sampler_name
-        self.b_scheduler = b_scheduler
-        self.c_compression = c_compression
-        self.b_latent = None
+        self.variation_seed = variation_seed
+        self.variation_strength = variation_strength
 
-    def stable_cascade_vae_encode(self, vae, pixels):
-        obj = comfy_extras.nodes_stable_cascade.StableCascade_StageC_VAEEncode()
-        stage_c, stage_b = obj.generate(pixels, vae, compression=self.c_compression)
-        self.b_latent = stage_b
-        return stage_c
+    def get_custom_noise(self, seed, noise, is_touched):
+        empty_noise = {'samples': torch.zeros(noise.size())}
+        if not is_touched:
+            noise = nodes_custom_sampler.Noise_RandomNoise(seed).generate_noise(empty_noise)
+        noise_2nd = nodes_custom_sampler.Noise_RandomNoise(self.variation_seed).generate_noise(empty_noise)
 
-    def stable_cascade_stage_b(self, image, positive, negative, latent):
-        # prepare stage_b
-        # self.b_latent['noise_mask'] = latent['noise_mask']
-        b_positive = comfy_extras.nodes_stable_cascade.StableCascade_StageB_Conditioning().set_prior(positive, latent)[0]
+        mixed_noise = ((1 - self.variation_strength) * noise + self.variation_strength * noise_2nd)
 
-        # stage_b sampling
-        b_latent = impact_sampling.ksampler_wrapper(self.b_model, self.b_seed, self.b_steps, self.b_cfg, self.b_sampler_name, self.b_scheduler, b_positive, negative, self.b_latent, 1.0)
+        # NOTE: Since the variance of the Gaussian noise in mixed_noise has changed, it must be corrected through scaling.
+        scale_factor = math.sqrt((1 - self.variation_strength) ** 2 + self.variation_strength ** 2)
+        corrected_noise = mixed_noise / scale_factor  # Scale the noise to maintain variance of 1
 
-        # stage_b decoding
-        self.b_latent = None
-        return self.b_vae.decode(b_latent['samples'])
+        return corrected_noise, True
 
 
 class SimpleDetailerDenoiseSchedulerHook(DetailerHook):

modules/impact/impact_sampling.py

@@ -2,9 +2,19 @@ import nodes
 from comfy.k_diffusion import sampling as k_diffusion_sampling
 from comfy import samplers
 from comfy_extras import nodes_custom_sampler
-
+import latent_preview
+import comfy
 import torch
 import math
+import comfy.model_management as mm
+
+
+try:
+    from comfy_extras.nodes_custom_sampler import Noise_EmptyNoise, Noise_RandomNoise
+    import node_helpers
+except:
+    print(f"\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n")
+    raise Exception("[Impact Pack] ComfyUI is an outdated version.")
 
 
 def calculate_sigmas(model, sampler, scheduler, steps):
@@ -13,7 +23,14 @@ def calculate_sigmas(model, sampler, scheduler, steps):
         steps += 1
         discard_penultimate_sigma = True
 
-    sigmas = samplers.calculate_sigmas_scheduler(model.model, scheduler, steps)
+    if scheduler.startswith('AYS'):
+        sigmas = nodes.NODE_CLASS_MAPPINGS['AlignYourStepsScheduler']().get_sigmas(scheduler[4:], steps, denoise=1.0)[0]
+    elif scheduler.startswith('GITS[coeff='):
+        sigmas = nodes.NODE_CLASS_MAPPINGS['GITSScheduler']().get_sigmas(float(scheduler[11:-1]), steps, denoise=1.0)[0]
+    elif scheduler == 'LTXV[default]':
+        sigmas = nodes.NODE_CLASS_MAPPINGS['LTXVScheduler']().get_sigmas(20, 2.05, 0.95, True, 0.1)[0]
+    else:
+        sigmas = samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, steps)
 
     if discard_penultimate_sigma:
         sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
@@ -75,7 +92,7 @@ def ksampler(sampler_name, total_sigmas, extra_options={}, inpaint_options={}):
             if noise_sampler is not None:
                 kwargs['noise_sampler'] = noise_sampler
 
-            return k_diffusion_sampling.sample_dpmpp_2m_sde(model, x, sigmas, **kwargs)
+            return k_diffusion_sampling.sample_dpmpp_3m_sde(model, x, sigmas, **kwargs)
 
         sampler_function = sample_dpmpp_sde
 
@@ -85,23 +102,109 @@ def ksampler(sampler_name, total_sigmas, extra_options={}, inpaint_options={}):
             if noise_sampler is not None:
                 kwargs['noise_sampler'] = noise_sampler
 
-            return k_diffusion_sampling.sample_dpmpp_2m_sde_gpu(model, x, sigmas, **kwargs)
+            return k_diffusion_sampling.sample_dpmpp_3m_sde_gpu(model, x, sigmas, **kwargs)
 
         sampler_function = sample_dpmpp_sde
+
     else:
-        return samplers.ksampler(sampler_name, extra_options, inpaint_options)
+        return comfy.samplers.sampler_object(sampler_name)
 
     return samplers.KSAMPLER(sampler_function, extra_options, inpaint_options)
 
 
+# modified version of SamplerCustom.sample
+def sample_with_custom_noise(model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image, noise=None, callback=None):
+    latent = latent_image
+    latent_image = latent["samples"]
+
+    if hasattr(comfy.sample, 'fix_empty_latent_channels'):
+        latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+
+    out = latent.copy()
+    out['samples'] = latent_image
+
+    if noise is None:
+        if not add_noise:
+            noise = Noise_EmptyNoise().generate_noise(out)
+        else:
+            noise = Noise_RandomNoise(noise_seed).generate_noise(out)
+
+    noise_mask = None
+    if "noise_mask" in latent:
+        noise_mask = latent["noise_mask"]
+
+    x0_output = {}
+    preview_callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+
+    if callback is not None:
+        def touched_callback(step, x0, x, total_steps):
+            callback(step, x0, x, total_steps)
+            preview_callback(step, x0, x, total_steps)
+    else:
+        touched_callback = preview_callback
+
+    disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+
+    device = mm.get_torch_device()
+
+    noise = noise.to(device)
+    latent_image = latent_image.to(device)
+    if noise_mask is not None:
+        noise_mask = noise_mask.to(device)
+
+    if negative != 'NegativePlaceholder':
+        # This way is incompatible with Advanced ControlNet, yet.
+        # guider = comfy.samplers.CFGGuider(model)
+        # guider.set_conds(positive, negative)
+        # guider.set_cfg(cfg)
+        samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image,
+                                             noise_mask=noise_mask, callback=touched_callback,
+                                             disable_pbar=disable_pbar, seed=noise_seed)
+    else:
+        guider = nodes_custom_sampler.Guider_Basic(model)
+        positive = node_helpers.conditioning_set_values(positive, {"guidance": cfg})
+        guider.set_conds(positive)
+        samples = guider.sample(noise, latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=touched_callback, disable_pbar=disable_pbar, seed=noise_seed)
+
+    samples = samples.to(comfy.model_management.intermediate_device())
+
+    out["samples"] = samples
+    if "x0" in x0_output:
+        out_denoised = latent.copy()
+        out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+    else:
+        out_denoised = out
+    return out, out_denoised
+
+
+# When sampling one step at a time, it mitigates the problem. (especially for _sde series samplers)
 def separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler, positive, negative,
-                     latent_image, start_at_step, end_at_step, return_with_leftover_noise, sigma_ratio=1.0, sampler_opt=None):
-    if sampler_opt is None:
-        total_sigmas = calculate_sigmas(model, sampler_name, scheduler, steps)
+                     latent_image, start_at_step, end_at_step, return_with_leftover_noise, sigma_ratio=1.0, sampler_opt=None, noise=None, callback=None, scheduler_func=None):
+
+    if scheduler_func is not None:
+        total_sigmas = scheduler_func(model, sampler_name, steps)
     else:
-        total_sigmas = calculate_sigmas(model, "", scheduler, steps)
+        if sampler_opt is None:
+            total_sigmas = calculate_sigmas(model, sampler_name, scheduler, steps)
+        else:
+            total_sigmas = calculate_sigmas(model, "", scheduler, steps)
+
+    sigmas = total_sigmas
+
+    if end_at_step is not None and end_at_step < (len(total_sigmas) - 1):
+        sigmas = total_sigmas[:end_at_step + 1]
+        if not return_with_leftover_noise:
+            sigmas[-1] = 0
+
+    if start_at_step is not None:
+        if start_at_step < (len(sigmas) - 1):
+            sigmas = sigmas[start_at_step:] * sigma_ratio
+        else:
+            if latent_image is not None:
+                return latent_image
+            else:
+                return {'samples': torch.zeros_like(noise)}
 
-    sigmas = total_sigmas[start_at_step:end_at_step+1] * sigma_ratio
     if sampler_opt is None:
         impact_sampler = ksampler(sampler_name, total_sigmas)
     else:
@@ -110,7 +213,7 @@ def separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler
     if len(sigmas) == 0 or (len(sigmas) == 1 and sigmas[0] == 0):
         return latent_image
     
-    res = nodes_custom_sampler.SamplerCustom().sample(model, add_noise, seed, cfg, positive, negative, impact_sampler, sigmas, latent_image)
+    res = sample_with_custom_noise(model, add_noise, seed, cfg, positive, negative, impact_sampler, sigmas, latent_image, noise=noise, callback=callback)
 
     if return_with_leftover_noise:
         return res[0]
@@ -118,11 +221,28 @@ def separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler
         return res[1]
 
 
+def impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, sigma_ratio=1.0, sampler_opt=None, noise=None, scheduler_func=None):
+    advanced_steps = math.floor(steps / denoise)
+    start_at_step = advanced_steps - steps
+    end_at_step = start_at_step + steps
+    return separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
+                            start_at_step, end_at_step, False, scheduler_func=scheduler_func)
+
+
 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):
+                     refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, sigma_factor=1.0, noise=None, scheduler_func=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:
-        refined_latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise * sigma_factor)[0]
+        # Use separated_sample instead of KSampler for `AYS scheduler`
+        # refined_latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise * sigma_factor)[0]
+
+        advanced_steps = math.floor(steps / denoise)
+        start_at_step = advanced_steps - steps
+        end_at_step = start_at_step + steps
+
+        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)
     else:
         advanced_steps = math.floor(steps / denoise)
         start_at_step = advanced_steps - steps
@@ -130,7 +250,8 @@ 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)
+                                       positive, negative, latent_image, start_at_step, end_at_step, True,
+                                       sigma_ratio=sigma_factor, noise=noise, scheduler_func=scheduler_func)
 
         if 'noise_mask' in latent_image:
             # noise_latent = \
@@ -143,7 +264,8 @@ 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)
+                                          refiner_positive, refiner_negative, temp_latent, end_at_step, advanced_steps + 1, False,
+                                          sigma_ratio=sigma_factor, scheduler_func=scheduler_func)
 
     return refined_latent
 
@@ -151,16 +273,17 @@ def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive,
 class KSamplerAdvancedWrapper:
     params = None
 
-    def __init__(self, model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=None, sigma_factor=1.0):
+    def __init__(self, model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=None, sigma_factor=1.0, scheduler_func=None):
         self.params = model, cfg, sampler_name, scheduler, positive, negative, sigma_factor
         self.sampler_opt = sampler_opt
+        self.scheduler_func = scheduler_func
 
     def clone_with_conditionings(self, positive, negative):
         model, cfg, sampler_name, scheduler, _, _, _ = self.params
         return KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, self.sampler_opt)
 
     def sample_advanced(self, add_noise, seed, steps, latent_image, start_at_step, end_at_step, return_with_leftover_noise, hook=None,
-                        recovery_mode="ratio additional", recovery_sampler="AUTO", recovery_sigma_ratio=1.0):
+                        recovery_mode="ratio additional", recovery_sampler="AUTO", recovery_sigma_ratio=1.0, noise=None):
 
         model, cfg, sampler_name, scheduler, positive, negative, sigma_factor = self.params
         # steps, start_at_step, end_at_step = self.compensate_denoise(steps, start_at_step, end_at_step)
@@ -184,7 +307,8 @@ class KSamplerAdvancedWrapper:
             if sigma_ratio > 0:
                 latent_image = separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
                                                 positive, negative, latent_image, start_at_step, end_at_step,
-                                                return_with_leftover_noise, sigma_ratio=sigma_ratio * sigma_factor, sampler_opt=self.sampler_opt)
+                                                return_with_leftover_noise, sigma_ratio=sigma_ratio * sigma_factor,
+                                                sampler_opt=self.sampler_opt, noise=noise, scheduler_func=self.scheduler_func)
         except ValueError as e:
             if str(e) == 'sigma_min and sigma_max must not be 0':
                 print(f"\nWARN: sampling skipped - sigma_min and sigma_max are 0")
@@ -207,8 +331,8 @@ class KSamplerAdvancedWrapper:
 
             try:
                 latent_image = separated_sample(model, add_noise, seed, steps, cfg, recovery_sampler, scheduler,
-                                                positive, negative, latent_image, start_at_step-compensate, end_at_step,
-                                                return_with_leftover_noise, sigma_ratio=recovery_sigma_ratio * sigma_factor, sampler_opt=self.sampler_opt)
+                                                positive, negative, latent_image, start_at_step-compensate, end_at_step, return_with_leftover_noise,
+                                                sigma_ratio=recovery_sigma_ratio * sigma_factor, sampler_opt=self.sampler_opt, scheduler_func=self.scheduler_func)
             except ValueError as e:
                 if str(e) == 'sigma_min and sigma_max must not be 0':
                     print(f"\nWARN: sampling skipped - sigma_min and sigma_max are 0")
@@ -219,8 +343,9 @@ class KSamplerAdvancedWrapper:
 class KSamplerWrapper:
     params = None
 
-    def __init__(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise):
+    def __init__(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, scheduler_func=None):
         self.params = model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
+        self.scheduler_func = scheduler_func
 
     def sample(self, latent_image, hook=None):
         model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
@@ -229,4 +354,4 @@ class KSamplerWrapper:
             model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \
                 hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)
 
-        return nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)[0]
+        return impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, scheduler_func=self.scheduler_func)

modules/impact/impact_server.py

@@ -5,7 +5,6 @@ import traceback
 from aiohttp import web
 
 import impact
-import server
 import folder_paths
 
 import torchvision
@@ -22,37 +21,7 @@ import impact.wildcards as wildcards
 import comfy
 from io import BytesIO
 import random
-
-
-@server.PromptServer.instance.routes.post("/upload/temp")
-async def upload_image(request):
-    upload_dir = folder_paths.get_temp_directory()
-
-    if not os.path.exists(upload_dir):
-        os.makedirs(upload_dir)
-    
-    post = await request.post()
-    image = post.get("image")
-
-    if image and image.file:
-        filename = image.filename
-        if not filename:
-            return web.Response(status=400)
-
-        split = os.path.splitext(filename)
-        i = 1
-        while os.path.exists(os.path.join(upload_dir, filename)):
-            filename = f"{split[0]} ({i}){split[1]}"
-            i += 1
-
-        filepath = os.path.join(upload_dir, filename)
-
-        with open(filepath, "wb") as f:
-            f.write(image.file.read())
-        
-        return web.json_response({"name": filename})
-    else:
-        return web.Response(status=400)
+from server import PromptServer
 
 
 sam_predictor = None
@@ -91,7 +60,7 @@ def async_prepare_sam(image_dir, model_name, filename):
         sam_predictor.model.cpu()
 
 
-@server.PromptServer.instance.routes.post("/sam/prepare")
+@PromptServer.instance.routes.post("/sam/prepare")
 async def sam_prepare(request):
     global sam_predictor
     global last_prepare_data
@@ -127,9 +96,10 @@ async def sam_prepare(request):
         thread.start()
 
         print(f"[INFO] ComfyUI-Impact-Pack: SAM model loaded. ")
+    return web.Response(status=200)
 
 
-@server.PromptServer.instance.routes.post("/sam/release")
+@PromptServer.instance.routes.post("/sam/release")
 async def release_sam(request):
     global sam_predictor
 
@@ -140,7 +110,7 @@ async def release_sam(request):
     print(f"[INFO] ComfyUI-Impact-Pack: unloading SAM model")
 
 
-@server.PromptServer.instance.routes.post("/sam/detect")
+@PromptServer.instance.routes.post("/sam/detect")
 async def sam_detect(request):
     global sam_predictor
     with sam_lock:
@@ -193,13 +163,19 @@ async def sam_detect(request):
             return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/impact/wildcards/list")
+@PromptServer.instance.routes.get("/impact/wildcards/refresh")
+async def wildcards_refresh(request):
+    impact.wildcards.wildcard_load()
+    return web.Response(status=200)
+
+
+@PromptServer.instance.routes.get("/impact/wildcards/list")
 async def wildcards_list(request):
     data = {'data': impact.wildcards.get_wildcard_list()}
     return web.json_response(data)
 
 
-@server.PromptServer.instance.routes.post("/impact/wildcards")
+@PromptServer.instance.routes.post("/impact/wildcards")
 async def populate_wildcards(request):
     data = await request.json()
     populated = wildcards.process(data['text'], data.get('seed', None))
@@ -208,7 +184,7 @@ async def populate_wildcards(request):
 
 segs_picker_map = {}
 
-@server.PromptServer.instance.routes.get("/impact/segs/picker/count")
+@PromptServer.instance.routes.get("/impact/segs/picker/count")
 async def segs_picker_count(request):
     node_id = request.rel_url.query.get('id', '')
 
@@ -219,7 +195,7 @@ async def segs_picker_count(request):
     return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/impact/segs/picker/view")
+@PromptServer.instance.routes.get("/impact/segs/picker/view")
 async def segs_picker(request):
     node_id = request.rel_url.query.get('id', '')
     idx = int(request.rel_url.query.get('idx', ''))
@@ -236,7 +212,7 @@ async def segs_picker(request):
     return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/view/validate")
+@PromptServer.instance.routes.get("/view/validate")
 async def view_validate(request):
     if "filename" in request.rel_url.query:
         filename = request.rel_url.query["filename"]
@@ -257,7 +233,7 @@ async def view_validate(request):
     return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/impact/validate/pb_id_image")
+@PromptServer.instance.routes.get("/impact/validate/pb_id_image")
 async def view_validate(request):
     if "id" in request.rel_url.query:
         pb_id = request.rel_url.query["id"]
@@ -272,7 +248,7 @@ async def view_validate(request):
     return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/impact/set/pb_id_image")
+@PromptServer.instance.routes.get("/impact/set/pb_id_image")
 async def set_previewbridge_image(request):
     try:
         if "filename" in request.rel_url.query:
@@ -308,7 +284,7 @@ async def set_previewbridge_image(request):
     return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/impact/get/pb_id_image")
+@PromptServer.instance.routes.get("/impact/get/pb_id_image")
 async def get_previewbridge_image(request):
     if "id" in request.rel_url.query:
         pb_id = request.rel_url.query["id"]
@@ -320,7 +296,7 @@ async def get_previewbridge_image(request):
     return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/impact/view/pb_id_image")
+@PromptServer.instance.routes.get("/impact/view/pb_id_image")
 async def view_previewbridge_image(request):
     if "id" in request.rel_url.query:
         pb_id = request.rel_url.query["id"]
@@ -339,6 +315,8 @@ def onprompt_for_switch(json_data):
     inversed_switch_info = {}
     onprompt_switch_info = {}
     onprompt_cond_branch_info = {}
+    disabled_switch = set()
+
 
     for k, v in json_data['prompt'].items():
         if 'class_type' not in v:
@@ -346,13 +324,16 @@ def onprompt_for_switch(json_data):
 
         cls = v['class_type']
         if cls == 'ImpactInversedSwitch':
-            select_input = v['inputs']['select']
-            if isinstance(select_input, list) and len(select_input) == 2:
-                input_node = json_data['prompt'][select_input[0]]
-                if input_node['class_type'] == 'ImpactInt' and 'inputs' in input_node and 'value' in input_node['inputs']:
-                    inversed_switch_info[k] = input_node['inputs']['value']
-            else:
-                inversed_switch_info[k] = select_input
+            if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'select' in v['inputs']:
+                select_input = v['inputs']['select']
+                if isinstance(select_input, list) and len(select_input) == 2:
+                    input_node = json_data['prompt'][select_input[0]]
+                    if input_node['class_type'] == 'ImpactInt' and 'inputs' in input_node and 'value' in input_node['inputs']:
+                        inversed_switch_info[k] = input_node['inputs']['value']
+                    else:
+                        print(f"\n##### ##### #####\n[WARN] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactInversedSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs if 'select_on_prompt' is selected.\n##### ##### #####\n")
+                else:
+                    inversed_switch_info[k] = select_input
 
         elif cls in ['ImpactSwitch', 'LatentSwitch', 'SEGSSwitch', 'ImpactMakeImageList']:
             if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'select' in v['inputs']:
@@ -365,10 +346,14 @@ def onprompt_for_switch(json_data):
                         if isinstance(input_node['inputs']['select'], int):
                             onprompt_switch_info[k] = input_node['inputs']['select']
                         else:
-                            print(f"\n##### ##### #####\n[WARN] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs.\n##### ##### #####\n")
+                            print(f"\n##### ##### #####\n[WARN] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs if 'select_on_prompt' is selected.\n##### ##### #####\n")
                 else:
                     onprompt_switch_info[k] = select_input
 
+                if k in onprompt_switch_info and f'input{onprompt_switch_info[k]}' not in v['inputs']:
+                    # disconnect output
+                    disabled_switch.add(k)
+
         elif cls == 'ImpactConditionalBranchSelMode':
             if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'cond' in v['inputs']:
                 cond_input = v['inputs']['cond']
@@ -393,6 +378,9 @@ def onprompt_for_switch(json_data):
                     if vv[1] + 1 != inversed_switch_info[vv[0]]:
                         disable_targets.add(kk)
 
+                if vv[0] in disabled_switch:
+                    disable_targets.add(kk)
+
         if k in onprompt_switch_info:
             selected_slot_name = f"input{onprompt_switch_info[k]}"
             for kk, vv in v['inputs'].items():
@@ -430,9 +418,14 @@ def gc_preview_bridge_cache(json_data):
 
     for key in list(core.preview_bridge_cache.keys()):
         if key not in prompt_keys:
-            print(f"key deleted: {key}")
+            # print(f"key deleted [PB]: {key}")
             del core.preview_bridge_cache[key]
 
+    for key in list(core.preview_bridge_last_mask_cache.keys()):
+        if key not in prompt_keys:
+            # print(f"key deleted [PB_last_mask]: {key}")
+            del core.preview_bridge_last_mask_cache[key]
+
 
 def workflow_imagereceiver_update(json_data):
     prompt = json_data['prompt']
@@ -463,7 +456,7 @@ def regional_sampler_seed_update(json_data):
                 new_seed = random.randint(0, 1125899906842624)
 
             if new_seed is not None:
-                server.PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "seed_2nd", "type": "INT", "value": new_seed})
+                PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "seed_2nd", "type": "INT", "value": new_seed})
 
 
 def onprompt_populate_wildcards(json_data):
@@ -496,7 +489,7 @@ def onprompt_populate_wildcards(json_data):
                 inputs['populated_text'] = wildcards.process(inputs['wildcard_text'], input_seed)
                 inputs['mode'] = False
 
-                server.PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "populated_text", "type": "STRING", "value": inputs['populated_text']})
+                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 'extra_data' in json_data and 'extra_pnginfo' in json_data['extra_data']:
@@ -535,7 +528,7 @@ def onprompt_for_remote(json_data):
                         break
 
                     target_inputs[widget_name] = inputs['value']
-                    server.PromptServer.instance.send_sync("impact-node-feedback", {"node_id": node_id, "widget_name": widget_name, "type": widget_type, "value": inputs['value']})
+                    PromptServer.instance.send_sync("impact-node-feedback", {"node_id": node_id, "widget_name": widget_name, "type": widget_type, "value": inputs['value']})
 
 
 def onprompt(json_data):
@@ -547,10 +540,11 @@ def onprompt(json_data):
         gc_preview_bridge_cache(json_data)
         workflow_imagereceiver_update(json_data)
         regional_sampler_seed_update(json_data)
+        core.current_prompt = json_data
     except Exception as e:
         print(f"[WARN] ComfyUI-Impact-Pack: Error on prompt - several features will not work.\n{e}")
 
     return json_data
 
 
-server.PromptServer.instance.add_on_prompt_handler(onprompt)
+PromptServer.instance.add_on_prompt_handler(onprompt)

modules/impact/legacy_nodes.py

@@ -25,6 +25,8 @@ class MMDetLoader:
 
     CATEGORY = "ImpactPack/Legacy"
 
+    DEPRECATED = True
+
     def load_mmdet(self, model_name):
         mmdet_path = folder_paths.get_full_path("mmdets", model_name)
         model = mmdet_nodes.load_mmdet(mmdet_path)
@@ -52,6 +54,8 @@ class BboxDetectorForEach:
 
     CATEGORY = "ImpactPack/Legacy"
 
+    DEPRECATED = True
+
     @staticmethod
     def detect(bbox_model, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
         mmdet_results = mmdet_nodes.inference_bbox(bbox_model, image, threshold)
@@ -102,6 +106,8 @@ class SegmDetectorCombined:
 
     CATEGORY = "ImpactPack/Legacy"
 
+    DEPRECATED = True
+
     def doit(self, segm_model, image, threshold, dilation):
         mmdet_results = mmdet_nodes.inference_segm(image, segm_model, threshold)
         segmasks = core.create_segmasks(mmdet_results)
@@ -150,6 +156,8 @@ class SegmDetectorForEach:
 
     CATEGORY = "ImpactPack/Legacy"
 
+    DEPRECATED = True
+
     def doit(self, segm_model, image, threshold, dilation, crop_factor):
         mmdet_results = mmdet_nodes.inference_segm(image, segm_model, threshold)
         segmasks = core.create_segmasks(mmdet_results)
@@ -190,6 +198,8 @@ class SegsMaskCombine:
 
     CATEGORY = "ImpactPack/Legacy"
 
+    DEPRECATED = True
+
     @staticmethod
     def combine(segs, image):
         h = image.shape[1]
@@ -226,6 +236,8 @@ class MaskPainter(nodes.PreviewImage):
 
     CATEGORY = "ImpactPack/Legacy"
 
+    DEPRECATED = True
+
     def save_painted_images(self, images, filename_prefix="impact-mask",
                             prompt=None, extra_pnginfo=None, mask_image=None, image=None):
         if image == "#placeholder" or image['image_hash'] != id(images):

modules/impact/logics.py

@@ -2,13 +2,13 @@ import sys
 import time
 
 import execution
-import folder_paths
 import impact.impact_server
 from server import PromptServer
 from impact.utils import any_typ
 import impact.core as core
 import re
-
+import nodes
+import traceback
 
 class ImpactCompare:
     @classmethod
@@ -65,8 +65,8 @@ class ImpactConditionalBranch:
         return {
             "required": {
                 "cond": ("BOOLEAN",),
-                "tt_value": (any_typ,),
-                "ff_value": (any_typ,),
+                "tt_value": (any_typ,{"lazy": True}),
+                "ff_value": (any_typ,{"lazy": True}),
             },
         }
 
@@ -75,7 +75,13 @@ class ImpactConditionalBranch:
 
     RETURN_TYPES = (any_typ, )
 
-    def doit(self, cond, tt_value, ff_value):
+    def check_lazy_status(self, cond, tt_value=None, ff_value=None):
+        if cond and tt_value is None:
+            return ["tt_value"]
+        if not cond and ff_value is None:
+            return ["ff_value"]
+
+    def doit(self, cond, tt_value=None, ff_value=None):
         if cond:
             return (tt_value,)
         else:
@@ -85,11 +91,18 @@ class ImpactConditionalBranch:
 class ImpactConditionalBranchSelMode:
     @classmethod
     def INPUT_TYPES(cls):
-        return {
-            "required": {
+        if not core.is_execution_model_version_supported():
+            required_inputs = {
                 "cond": ("BOOLEAN",),
                 "sel_mode": ("BOOLEAN", {"default": True, "label_on": "select_on_prompt", "label_off": "select_on_execution"}),
-            },
+                }
+        else:
+            required_inputs = {
+                "cond": ("BOOLEAN",),
+                }
+
+        return {
+            "required": required_inputs,
             "optional": {
                 "tt_value": (any_typ,),
                 "ff_value": (any_typ,),
@@ -101,7 +114,7 @@ class ImpactConditionalBranchSelMode:
 
     RETURN_TYPES = (any_typ, )
 
-    def doit(self, cond, sel_mode, tt_value=None, ff_value=None):
+    def doit(self, cond, tt_value=None, ff_value=None, **kwargs):
         print(f'tt={tt_value is None}\nff={ff_value is None}')
         if cond:
             return (tt_value,)
@@ -149,7 +162,7 @@ class ImpactIfNone:
             "optional": {"signal": (any_typ,), "any_input": (any_typ,), }
         }
 
-    RETURN_TYPES = (any_typ, "BOOLEAN", )
+    RETURN_TYPES = (any_typ, "BOOLEAN")
     RETURN_NAMES = ("signal_opt", "bool")
     FUNCTION = "doit"
 
@@ -259,6 +272,24 @@ class ImpactFloat:
         return (value, )
 
 
+class ImpactBoolean:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("BOOLEAN", {"default": False}),
+            },
+        }
+
+    FUNCTION = "doit"
+    CATEGORY = "ImpactPack/Logic"
+
+    RETURN_TYPES = ("BOOLEAN", )
+
+    def doit(self, value):
+        return (value, )
+
+
 class ImpactValueSender:
     @classmethod
     def INPUT_TYPES(cls):
@@ -617,88 +648,150 @@ class ImpactControlBridge:
     def INPUT_TYPES(cls):
         return {"required": {
                       "value": (any_typ,),
-                      "mode": ("BOOLEAN", {"default": True, "label_on": "Active", "label_off": "Mute/Bypass"}),
-                      "behavior": ("BOOLEAN", {"default": True, "label_on": "Mute", "label_off": "Bypass"}),
+                      "mode": ("BOOLEAN", {"default": True, "label_on": "Active", "label_off": "Stop/Mute/Bypass"}),
+                      "behavior": (["Stop", "Mute", "Bypass"], ),
                     },
                 "hidden": {"unique_id": "UNIQUE_ID", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}
                 }
 
     FUNCTION = "doit"
 
-    CATEGORY = "ImpactPack/Logic/_for_test"
+    CATEGORY = "ImpactPack/Logic"
     RETURN_TYPES = (any_typ,)
     RETURN_NAMES = ("value",)
     OUTPUT_NODE = True
 
+    DESCRIPTION = ("When behavior is Stop and mode is active, the input value is passed directly to the output.\n"
+                   "When behavior is Mute/Bypass and mode is active, the node connected to the output is changed to active state.\n"
+                   "When behavior is Stop and mode is Stop/Mute/Bypass, the workflow execution of the current node is halted.\n"
+                   "When behavior is Mute/Bypass and mode is Stop/Mute/Bypass, the node connected to the output is changed to Mute/Bypass state.")
+
     @classmethod
-    def IS_CHANGED(self, value, mode, behavior=True, unique_id=None, prompt=None, extra_pnginfo=None):
-        nodes, links = workflow_to_map(extra_pnginfo['workflow'])
+    def IS_CHANGED(self, value, mode, behavior="Stop", unique_id=None, prompt=None, extra_pnginfo=None):
+        if behavior == "Stop":
+            return value, mode, behavior
+        else:
+            # NOTE: extra_pnginfo is not populated for IS_CHANGED.
+            #       so extra_pnginfo is useless in here
+            try:
+                workflow = core.current_prompt['extra_data']['extra_pnginfo']['workflow']
+            except:
+                print(f"[Impact Pack] core.current_prompt['extra_data']['extra_pnginfo']['workflow']")
+                return 0
 
-        next_nodes = []
+            nodes, links = workflow_to_map(workflow)
+            next_nodes = []
 
-        for link in nodes[unique_id]['outputs'][0]['links']:
-            node_id = str(links[link][2])
-            impact.utils.collect_non_reroute_nodes(nodes, links, next_nodes, node_id)
+            for link in nodes[unique_id]['outputs'][0]['links']:
+                node_id = str(links[link][2])
+                impact.utils.collect_non_reroute_nodes(nodes, links, next_nodes, node_id)
 
         return next_nodes
 
-
-    def doit(self, value, mode, behavior=True, unique_id=None, prompt=None, extra_pnginfo=None):
+    def doit(self, value, mode, behavior="Stop", unique_id=None, prompt=None, extra_pnginfo=None):
         global error_skip_flag
 
-        nodes, links = workflow_to_map(extra_pnginfo['workflow'])
-
-        active_nodes = []
-        mute_nodes = []
-        bypass_nodes = []
-
-        for link in nodes[unique_id]['outputs'][0]['links']:
-            node_id = str(links[link][2])
-
-            next_nodes = []
-            impact.utils.collect_non_reroute_nodes(nodes, links, next_nodes, node_id)
-
-            for next_node_id in next_nodes:
-                node_mode = nodes[next_node_id]['mode']
-
-                if node_mode == 0:
-                    active_nodes.append(next_node_id)
-                elif node_mode == 2:
-                    mute_nodes.append(next_node_id)
-                elif node_mode == 4:
-                    bypass_nodes.append(next_node_id)
-
-        if mode:
-            # active
-            should_be_active_nodes = mute_nodes + bypass_nodes
-            if len(should_be_active_nodes) > 0:
-                PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'actives': list(should_be_active_nodes)})
-                error_skip_flag = True
-                raise Exception("IMPACT-PACK-SIGNAL: STOP CONTROL BRIDGE\nIf you see this message, your ComfyUI-Manager is outdated. Please update it.")
-
-        elif behavior:
-            # mute
-            should_be_mute_nodes = active_nodes + bypass_nodes
-            if len(should_be_mute_nodes) > 0:
-                PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'mutes': list(should_be_mute_nodes)})
-                error_skip_flag = True
-                raise Exception("IMPACT-PACK-SIGNAL: STOP CONTROL BRIDGE\nIf you see this message, your ComfyUI-Manager is outdated. Please update it.")
-
+        if core.is_execution_model_version_supported():
+            from comfy_execution.graph import ExecutionBlocker
         else:
-            # bypass
-            should_be_bypass_nodes = active_nodes + mute_nodes
-            if len(should_be_bypass_nodes) > 0:
-                PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'bypasses': list(should_be_bypass_nodes)})
-                error_skip_flag = True
-                raise Exception("IMPACT-PACK-SIGNAL: STOP CONTROL BRIDGE\nIf you see this message, your ComfyUI-Manager is outdated. Please update it.")
+            print("[Impact Pack] ImpactControlBridge: ComfyUI is outdated. The 'Stop' behavior cannot function properly.")
 
-        return (value, )
+        if behavior == "Stop":
+            if mode:
+                return (value, )
+            else:
+                return (ExecutionBlocker(None), )
+        else:
+            workflow_nodes, links = workflow_to_map(extra_pnginfo['workflow'])
+
+            active_nodes = []
+            mute_nodes = []
+            bypass_nodes = []
+
+            for link in workflow_nodes[unique_id]['outputs'][0]['links']:
+                node_id = str(links[link][2])
+
+                next_nodes = []
+                impact.utils.collect_non_reroute_nodes(workflow_nodes, links, next_nodes, node_id)
+
+                for next_node_id in next_nodes:
+                    node_mode = workflow_nodes[next_node_id]['mode']
+
+                    if node_mode == 0:
+                        active_nodes.append(next_node_id)
+                    elif node_mode == 2:
+                        mute_nodes.append(next_node_id)
+                    elif node_mode == 4:
+                        bypass_nodes.append(next_node_id)
+
+            if mode:
+                # active
+                should_be_active_nodes = mute_nodes + bypass_nodes
+                if len(should_be_active_nodes) > 0:
+                    PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'actives': list(should_be_active_nodes)})
+                    nodes.interrupt_processing()
+
+            elif behavior == "Mute" or behavior == True:
+                # mute
+                should_be_mute_nodes = active_nodes + bypass_nodes
+                if len(should_be_mute_nodes) > 0:
+                    PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'mutes': list(should_be_mute_nodes)})
+                    nodes.interrupt_processing()
+
+            else:
+                # bypass
+                should_be_bypass_nodes = active_nodes + mute_nodes
+                if len(should_be_bypass_nodes) > 0:
+                    PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'bypasses': list(should_be_bypass_nodes)})
+                    nodes.interrupt_processing()
+
+            return (value, )
+
+
+class ImpactExecutionOrderController:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                    "signal": (any_typ,),
+                    "value": (any_typ,),
+                    }}
+
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+    RETURN_TYPES = (any_typ, any_typ)
+    RETURN_NAMES = ("signal", "value")
+
+    def doit(self, signal, value):
+        return signal, value
+
+
+class ImpactListBridge:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                    "list_input": (any_typ,),
+                    }}
+
+    FUNCTION = "doit"
+
+    DESCRIPTION = "When passing the list output through this node, it collects and organizes the data before forwarding it, which ensures that the previous stage's sub-workflow has been completed."
+
+    CATEGORY = "ImpactPack/Util"
+    RETURN_TYPES = (any_typ, )
+    RETURN_NAMES = ("list_output", )
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True, )
+
+    @staticmethod
+    def doit(list_input):
+        return (list_input,)
 
 
 original_handle_execution = execution.PromptExecutor.handle_execution_error
 
 
 def handle_execution_error(**kwargs):
-    print(f" handled")
     execution.PromptExecutor.handle_execution_error(**kwargs)
 

modules/impact/pipe.py

@@ -1,5 +1,7 @@
 import folder_paths
 import impact.wildcards
+from impact.utils import any_typ
+
 
 class ToDetailerPipe:
     @classmethod
@@ -108,6 +110,23 @@ class FromDetailerPipe_SDXL:
         return detailer_pipe, model, clip, vae, positive, negative, bbox_detector, sam_model_opt, segm_detector_opt, detailer_hook, refiner_model, refiner_clip, refiner_positive, refiner_negative
 
 
+class AnyPipeToBasic:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {"any_pipe": (any_typ,)},
+        }
+
+    RETURN_TYPES = ("BASIC_PIPE", )
+    RETURN_NAMES = ("basic_pipe", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Pipe"
+
+    def doit(self, any_pipe):
+        return (any_pipe[:5], )
+
+
 class ToBasicPipe:
     @classmethod
     def INPUT_TYPES(s):

modules/impact/segs_nodes.py

@@ -5,10 +5,20 @@ import impact.impact_server
 from nodes import MAX_RESOLUTION
 
 from impact.utils import *
-import impact.core as core
-from impact.core import SEG
+from . import core
+from .core import SEG
 import impact.utils as utils
 from . import defs
+from . import segs_upscaler
+from comfy.cli_args import args
+import math
+
+
+try:
+    from comfy_extras import nodes_differential_diffusion
+except Exception:
+    print(f"\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n")
+    raise Exception("[Impact Pack] ComfyUI is an outdated version.")
 
 
 class SEGSDetailer:
@@ -17,17 +27,17 @@ class SEGSDetailer:
         return {"required": {
                      "image": ("IMAGE", ),
                      "segs": ("SEGS", ),
-                     "guide_size": ("FLOAT", {"default": 256, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
+                     "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
                      "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
                      "max_size": ("FLOAT", {"default": 768, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
                      "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                      "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                      "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                      "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
-                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
+                     "scheduler": (core.SCHEDULERS,),
                      "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
                      "noise_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
-                     "force_inpaint": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                     "force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
                      "basic_pipe": ("BASIC_PIPE",),
                      "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
                      "batch_size": ("INT", {"default": 1, "min": 1, "max": 100}),
@@ -37,7 +47,8 @@ class SEGSDetailer:
                 "optional": {
                      "refiner_basic_pipe_opt": ("BASIC_PIPE",),
                      "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
-                     "noise_mask_feather": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
+                     "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
+                     "scheduler_func_opt": ("SCHEDULER_FUNC",),
                      }
                 }
 
@@ -52,7 +63,7 @@ class SEGSDetailer:
     @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,
-                  refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0):
+                  refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
 
         model, clip, vae, positive, negative = basic_pipe
         if refiner_basic_pipe_opt is None:
@@ -65,6 +76,9 @@ class SEGSDetailer:
         new_segs = []
         cnet_pil_list = []
 
+        if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
+            model = nodes_differential_diffusion.DifferentialDiffusion().apply(model)[0]
+
         for i in range(batch_size):
             seed += 1
             for seg in segs[1]:
@@ -83,13 +97,29 @@ class SEGSDetailer:
                 else:
                     cropped_mask = None
 
+                cropped_positive = [
+                    [condition, {
+                        k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v
+                        for k, v in details.items()
+                    }]
+                    for condition, details in positive
+                ]
+
+                cropped_negative = [
+                    [condition, {
+                        k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v
+                        for k, v in details.items()
+                    }]
+                    for condition, details in negative
+                ]
+
                 enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for, max_size,
                                                                 seg.bbox, seed, steps, cfg, sampler_name, scheduler,
-                                                                positive, negative, denoise, cropped_mask, force_inpaint,
+                                                                cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint,
                                                                 refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                                                                 refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
                                                                 control_net_wrapper=seg.control_net_wrapper, cycle=cycle,
-                                                                inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
+                                                                inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt)
 
                 if cnet_pils is not None:
                     cnet_pil_list.extend(cnet_pils)
@@ -106,7 +136,7 @@ class SEGSDetailer:
 
     def doit(self, 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,
-             refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0):
+             refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
 
         if len(image) > 1:
             raise Exception('[Impact Pack] ERROR: SEGSDetailer 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.')
@@ -114,13 +144,13 @@ class SEGSDetailer:
         segs, cnet_pil_list = SEGSDetailer.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, batch_size, cycle=cycle,
                                                      refiner_basic_pipe_opt=refiner_basic_pipe_opt,
-                                                     inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
+                                                     inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
 
         # set fallback image
         if len(cnet_pil_list) == 0:
             cnet_pil_list = [empty_pil_tensor()]
 
-        return (segs, cnet_pil_list)
+        return segs, cnet_pil_list
 
 
 class SEGSPaste:
@@ -176,6 +206,11 @@ class SEGSPaste:
 
                     mask = tensor_gaussian_blur_mask(mask, feather) * (alpha/255)
                     x, y, *_ = seg.crop_region
+
+                    # ensure same device
+                    mask = mask.to(image_i.device)
+                    ref_image = ref_image.to(image_i.device)
+
                     tensor_paste(image_i, ref_image, (x, y), mask)
 
             if result is None:
@@ -183,6 +218,9 @@ class SEGSPaste:
             else:
                 result = torch.concat((result, image_i), dim=0)
 
+        if not args.highvram and not args.gpu_only:
+            result = result.cpu()
+
         return (result, )
 
 
@@ -458,7 +496,7 @@ class SEGSOrderedFilter:
     def INPUT_TYPES(s):
         return {"required": {
                         "segs": ("SEGS", ),
-                        "target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2"],),
+                        "target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2", "confidence"],),
                         "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}),
@@ -492,8 +530,12 @@ class SEGSOrderedFilter:
                 value = x2
             elif target == "y1":
                 value = y1
-            else:
+            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))
 
@@ -511,7 +553,7 @@ class SEGSOrderedFilter:
             else:
                 remained_list.append(item[1])
 
-        return ((segs[0], result_list), (segs[0], remained_list), )
+        return (segs[0], result_list), (segs[0], remained_list),
 
 
 class SEGSRangeFilter:
@@ -519,7 +561,7 @@ class SEGSRangeFilter:
     def INPUT_TYPES(s):
         return {"required": {
                         "segs": ("SEGS", ),
-                        "target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2", "length_percent"],),
+                        "target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2", "length_percent", "confidence(0-100)"],),
                         "mode": ("BOOLEAN", {"default": True, "label_on": "inside", "label_off": "outside"}),
                         "min_value": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
                         "max_value": ("INT", {"default": 67108864, "min": 0, "max": sys.maxsize, "step": 1}),
@@ -559,8 +601,12 @@ class SEGSRangeFilter:
                 value = x2
             elif target == "y1":
                 value = y1
-            else:
+            elif target == "y2":
                 value = y2
+            elif target == "confidence(0-100)":
+                value = seg.confidence*100
+            else:
+                raise Exception(f"[Impact Pack] SEGSRangeFilter - Unexpected target '{target}'")
 
             if mode and min_value <= value <= max_value:
                 print(f"[in] value={value} / {mode}, {min_value}, {max_value}")
@@ -572,7 +618,7 @@ class SEGSRangeFilter:
                 remained_segs.append(seg)
                 print(f"[filter] value={value} / {mode}, {min_value}, {max_value}")
 
-        return ((segs[0], new_segs), (segs[0], remained_segs), )
+        return (segs[0], new_segs), (segs[0], remained_segs),
 
 
 class SEGSToImageList:
@@ -658,6 +704,68 @@ class SEGSToMaskBatch:
         return (mask_batch,)
 
 
+class SEGSMerge:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                     "segs": ("SEGS", ),
+                     },
+                }
+
+    RETURN_TYPES = ("SEGS",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    DESCRIPTION = "SEGS contains multiple SEGs. SEGS Merge integrates several SEGs into a single merged SEG. The label is changed to `merged` and the confidence becomes the minimum confidence. The applied controlnet and cropped_image are removed."
+
+    def doit(self, segs):
+        crop_left = sys.maxsize
+        crop_right = 0
+        crop_top = sys.maxsize
+        crop_bottom = 0
+
+        bbox_left = sys.maxsize
+        bbox_right = 0
+        bbox_top = sys.maxsize
+        bbox_bottom = 0
+
+        min_confidence = 1.0
+
+        for seg in segs[1]:
+            cx1 = seg.crop_region[0]
+            cy1 = seg.crop_region[1]
+            cx2 = seg.crop_region[2]
+            cy2 = seg.crop_region[3]
+
+            bx1 = seg.bbox[0]
+            by1 = seg.bbox[1]
+            bx2 = seg.bbox[2]
+            by2 = seg.bbox[3]
+
+            crop_left = min(crop_left, cx1)
+            crop_top = min(crop_top, cy1)
+            crop_right = max(crop_right, cx2)
+            crop_bottom = max(crop_bottom, cy2)
+
+            bbox_left = min(bbox_left, bx1)
+            bbox_top = min(bbox_top, by1)
+            bbox_right = max(bbox_right, bx2)
+            bbox_bottom = max(bbox_bottom, by2)
+
+            min_confidence = min(min_confidence, seg.confidence)
+        
+        combined_mask = core.segs_to_combined_mask(segs)
+        cropped_mask = combined_mask[crop_top:crop_bottom, crop_left:crop_right]
+        cropped_mask = cropped_mask.unsqueeze(0)
+
+        crop_region = [crop_left, crop_top, crop_right, crop_bottom]
+        bbox = [bbox_left, bbox_top, bbox_right, bbox_bottom]
+
+        seg = SEG(None, cropped_mask, min_confidence, crop_region, bbox, 'merged', None)
+        return ((segs[0], [seg]),)
+        
+
 class SEGSConcat:
     @classmethod
     def INPUT_TYPES(s):
@@ -695,6 +803,23 @@ class SEGSConcat:
             return ((dim, res), )
 
 
+class Count_Elts_in_SEGS:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                     "segs": ("SEGS", ),
+                     },
+                }
+
+    RETURN_TYPES = ("INT",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, segs):
+        return (len(segs[1]), )
+
+
 class DecomposeSEGS:
     @classmethod
     def INPUT_TYPES(s):
@@ -755,6 +880,44 @@ class From_SEG_ELT:
         return (seg_elt, cropped_image, to_tensor(seg_elt.cropped_mask), seg_elt.crop_region, seg_elt.bbox, seg_elt.control_net_wrapper, seg_elt.confidence, seg_elt.label,)
 
 
+class From_SEG_ELT_bbox:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                     "bbox": ("SEG_ELT_bbox", ),
+                     },
+                }
+
+    RETURN_TYPES = ("INT", "INT", "INT", "INT")
+    RETURN_NAMES = ("left", "top", "right", "bottom")
+
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, bbox):
+        return [int(c) for c in bbox]
+
+
+class From_SEG_ELT_crop_region:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                     "crop_region": ("SEG_ELT_crop_region", ),
+                     },
+                }
+
+    RETURN_TYPES = ("INT", "INT", "INT", "INT")
+    RETURN_NAMES = ("left", "top", "right", "bottom")
+
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, crop_region):
+        return crop_region
+
+
 class Edit_SEG_ELT:
     @classmethod
     def INPUT_TYPES(s):
@@ -938,10 +1101,10 @@ class SEG_ELT_BBOX_ScaleBy:
         x1, y1, x2, y2 = x1-cx1, y1-cy1, x2-cx1, y2-cy1
         h, w = mask.shape
 
-        x1 = min(w-1, max(0, x1))
-        x2 = min(w-1, max(0, x2))
-        y1 = min(h-1, max(0, y1))
-        y2 = min(h-1, max(0, y2))
+        x1 = int(min(w-1, max(0, x1)))
+        x2 = int(min(w-1, max(0, x2)))
+        y1 = int(min(h-1, max(0, y1)))
+        y2 = int(min(h-1, max(0, y2)))
 
         mask_cropped = mask.copy()
         mask_cropped[:, :x1] = 0  # zero fill left side
@@ -1079,10 +1242,11 @@ class MaskToSEGS:
 
     CATEGORY = "ImpactPack/Operation"
 
-    def doit(self, mask, combined, crop_factor, bbox_fill, drop_size, contour_fill=False):
+    @staticmethod
+    def doit(mask, combined, crop_factor, bbox_fill, drop_size, contour_fill=False):
         mask = make_2d_mask(mask)
-
         result = core.mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size, is_contour=contour_fill)
+
         return (result, )
 
 
@@ -1104,11 +1268,17 @@ class MaskToSEGS_for_AnimateDiff:
 
     CATEGORY = "ImpactPack/Operation"
 
-    def doit(self, mask, combined, crop_factor, bbox_fill, drop_size, contour_fill=False):
+    @staticmethod
+    def doit(mask, combined, crop_factor, bbox_fill, drop_size, contour_fill=False):
+        if (len(mask.shape) == 4 and mask.shape[1] > 1) or (len(mask.shape) == 3 and mask.shape[0] > 1):
+            mask = make_3d_mask(mask)
+            if contour_fill:
+                print(f"[Impact Pack] MaskToSEGS_for_AnimateDiff: 'contour_fill' is ignored because batch mask 'contour_fill' is not supported.")
+            result = core.batch_mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size)
+            return (result, )
+
         mask = make_2d_mask(mask)
-
         segs = core.mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size, is_contour=contour_fill)
-
         all_masks = SEGSToMaskList().doit(segs)[0]
 
         result_mask = (all_masks[0] * 255).to(torch.uint8)
@@ -1118,7 +1288,7 @@ class MaskToSEGS_for_AnimateDiff:
         result_mask = (result_mask/255.0).to(torch.float32)
         result_mask = utils.to_binary_mask(result_mask, 0.1)[0]
 
-        return MaskToSEGS().doit(result_mask, False, crop_factor, False, drop_size, contour_fill)
+        return MaskToSEGS.doit(result_mask, False, crop_factor, False, drop_size, contour_fill)
 
 
 class IPAdapterApplySEGS:
@@ -1137,7 +1307,11 @@ class IPAdapterApplySEGS:
                     "weight_v2": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}),
                     "context_crop_factor": ("FLOAT", {"default": 1.2, "min": 1.0, "max": 100, "step": 0.1}),
                     "reference_image": ("IMAGE",),
-                    }
+                    },
+                "optional": {
+                    "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
+                    "neg_image": ("IMAGE",),
+                    },
                 }
 
     RETURN_TYPES = ("SEGS",)
@@ -1145,7 +1319,8 @@ class IPAdapterApplySEGS:
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, segs, ipadapter_pipe, weight, noise, weight_type, start_at, end_at, unfold_batch, faceid_v2, weight_v2, context_crop_factor, reference_image):
+    @staticmethod
+    def doit(segs, ipadapter_pipe, weight, noise, weight_type, start_at, end_at, unfold_batch, faceid_v2, weight_v2, context_crop_factor, reference_image, combine_embeds="concat", neg_image=None):
 
         if len(ipadapter_pipe) == 4:
             print(f"[Impact Pack] IPAdapterApplySEGS: Installed Inspire Pack is outdated.")
@@ -1163,7 +1338,7 @@ class IPAdapterApplySEGS:
             context_crop_region = make_crop_region(w, h, seg.crop_region, context_crop_factor)
             cropped_image = crop_image(reference_image, context_crop_region)
 
-            control_net_wrapper = core.IPAdapterWrapper(ipadapter_pipe, weight, noise, weight_type, start_at, end_at, unfold_batch, faceid_v2, weight_v2, cropped_image, prev_control_net=seg.control_net_wrapper)
+            control_net_wrapper = core.IPAdapterWrapper(ipadapter_pipe, weight, noise, weight_type, start_at, end_at, unfold_batch, weight_v2, cropped_image, neg_image=neg_image, prev_control_net=seg.control_net_wrapper, combine_embeds=combine_embeds)
             new_seg = SEG(seg.cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, control_net_wrapper)
             new_segs.append(new_seg)
 
@@ -1187,9 +1362,12 @@ class ControlNetApplySEGS:
     RETURN_TYPES = ("SEGS",)
     FUNCTION = "doit"
 
+    DEPRECATED = True
+
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, segs, control_net, strength, segs_preprocessor=None, control_image=None):
+    @staticmethod
+    def doit(segs, control_net, strength, segs_preprocessor=None, control_image=None):
         new_segs = []
 
         for seg in segs[1]:
@@ -1213,7 +1391,8 @@ class ControlNetApplyAdvancedSEGS:
                     },
                 "optional": {
                     "segs_preprocessor": ("SEGS_PREPROCESSOR",),
-                    "control_image": ("IMAGE",)
+                    "control_image": ("IMAGE",),
+                    "vae": ("VAE",)
                     }
                 }
 
@@ -1222,13 +1401,14 @@ class ControlNetApplyAdvancedSEGS:
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, segs, control_net, strength, start_percent, end_percent, segs_preprocessor=None, control_image=None):
+    @staticmethod
+    def doit(segs, control_net, strength, start_percent, end_percent, segs_preprocessor=None, control_image=None, vae=None):
         new_segs = []
 
         for seg in segs[1]:
             control_net_wrapper = core.ControlNetAdvancedWrapper(control_net, strength, start_percent, end_percent, segs_preprocessor,
                                                                  seg.control_net_wrapper, original_size=segs[0], crop_region=seg.crop_region,
-                                                                 control_image=control_image)
+                                                                 control_image=control_image, vae=vae)
             new_seg = SEG(seg.cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, control_net_wrapper)
             new_segs.append(new_seg)
 
@@ -1245,7 +1425,8 @@ class ControlNetClearSEGS:
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, segs):
+    @staticmethod
+    def doit(segs):
         new_segs = []
 
         for seg in segs[1]:
@@ -1297,13 +1478,12 @@ class SEGSPicker:
 
     RETURN_TYPES = ("SEGS", )
 
-    OUTPUT_NODE = True
-
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, picks, segs, fallback_image_opt=None, unique_id=None):
+    @staticmethod
+    def doit(picks, segs, fallback_image_opt=None, unique_id=None):
         if fallback_image_opt is not None:
             segs = core.segs_scale_match(segs, fallback_image_opt.shape)
 
@@ -1318,7 +1498,7 @@ class SEGSPicker:
             else:
                 cropped_image = empty_pil_tensor()
 
-            mask_array = seg.cropped_mask
+            mask_array = seg.cropped_mask.copy()
             mask_array[mask_array < 0.3] = 0.3
             mask_array = mask_array[None, ..., None]
             cropped_image = cropped_image * mask_array
@@ -1358,7 +1538,8 @@ class DefaultImageForSEGS:
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, segs, image, override):
+    @staticmethod
+    def doit(segs, image, override):
         results = []
 
         segs = core.segs_scale_match(segs, image.shape)
@@ -1402,7 +1583,8 @@ class RemoveImageFromSEGS:
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, segs):
+    @staticmethod
+    def doit(segs):
         results = []
 
         if len(segs[1]) > 0:
@@ -1421,7 +1603,7 @@ class MakeTileSEGS:
         return {"required": {
                      "images": ("IMAGE", ),
                      "bbox_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 8}),
-                     "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}),
+                     "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.01}),
                      "min_overlap": ("INT", {"default": 5, "min": 0, "max": 512, "step": 1}),
                      "filter_segs_dilation": ("INT", {"default": 20, "min": -255, "max": 255, "step": 1}),
                      "mask_irregularity": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
@@ -1439,9 +1621,10 @@ class MakeTileSEGS:
 
     CATEGORY = "ImpactPack/__for_testing"
 
-    def doit(self, images, bbox_size, crop_factor, min_overlap, filter_segs_dilation, mask_irregularity=0, irregular_mask_mode="Reuse fast", filter_in_segs_opt=None, filter_out_segs_opt=None):
+    @staticmethod
+    def doit(images, bbox_size, crop_factor, min_overlap, filter_segs_dilation, mask_irregularity=0, irregular_mask_mode="Reuse fast", filter_in_segs_opt=None, filter_out_segs_opt=None):
         if bbox_size <= 2*min_overlap:
-            new_min_overlap = 2 / bbox_size
+            new_min_overlap = bbox_size / 2
             print(f"[MakeTileSEGS] min_overlap should be greater than bbox_size. (value changed: {min_overlap} => {new_min_overlap})")
             min_overlap = new_min_overlap
 
@@ -1461,6 +1644,12 @@ class MakeTileSEGS:
             elif irregular_mask_mode == "All random fast":
                 mask_quality = 512
 
+        # compensate overlap/bbox_size for irregular mask
+        if mask_irregularity > 0:
+            compensate = max(6, int(mask_quality * mask_irregularity / 4))
+            min_overlap += compensate
+            bbox_size += compensate*2
+
         # create exclusion mask
         if filter_out_segs_opt is not None:
             exclusion_mask = core.segs_to_combined_mask(filter_out_segs_opt)
@@ -1478,7 +1667,7 @@ class MakeTileSEGS:
 
             a, b = core.mask_to_segs(and_mask, True, 1.0, False, 0)
             if len(b) == 0:
-                return a, b
+                return ((a, b),)
 
             start_x, start_y, c, d = b[0].crop_region
             w = c - start_x
@@ -1495,8 +1684,8 @@ class MakeTileSEGS:
             print(f"[MaskTileSEGS] bbox_size is greater than resolution (value changed: {bbox_size} => {new_bbox_size}")
             bbox_size = new_bbox_size
 
-        n_horizontal = int(w / (bbox_size - min_overlap))
-        n_vertical = int(h / (bbox_size - min_overlap))
+        n_horizontal = math.ceil(w / (bbox_size - min_overlap))
+        n_vertical = math.ceil(h / (bbox_size - min_overlap))
 
         w_overlap_sum = (bbox_size * n_horizontal) - w
         if w_overlap_sum < 0:
@@ -1514,6 +1703,12 @@ class MakeTileSEGS:
 
         new_segs = []
 
+        if w_overlap_size == bbox_size:
+            n_horizontal = 1
+
+        if h_overlap_size == bbox_size:
+            n_vertical = 1
+
         y = start_y
         for j in range(0, n_vertical):
             x = start_x
@@ -1590,3 +1785,134 @@ class MakeTileSEGS:
 
         res = (ih, iw), new_segs  # segs
         return (res,)
+
+
+class SEGSUpscaler:
+    @classmethod
+    def INPUT_TYPES(s):
+        resampling_methods = ["lanczos", "nearest", "bilinear", "bicubic"]
+
+        return {"required": {
+                    "image": ("IMAGE",),
+                    "segs": ("SEGS",),
+                    "model": ("MODEL",),
+                    "clip": ("CLIP",),
+                    "vae": ("VAE",),
+                    "rescale_factor": ("FLOAT", {"default": 2, "min": 0.01, "max": 100.0, "step": 0.01}),
+                    "resampling_method": (resampling_methods,),
+                    "supersample": (["true", "false"],),
+                    "rounding_modulus": ("INT", {"default": 8, "min": 8, "max": 1024, "step": 8}),
+                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+                    "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
+                    "scheduler": (core.SCHEDULERS,),
+                    "positive": ("CONDITIONING",),
+                    "negative": ("CONDITIONING",),
+                    "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
+                    "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
+                    "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                    "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
+                    },
+                "optional": {
+                    "upscale_model_opt": ("UPSCALE_MODEL",),
+                    "upscaler_hook_opt": ("UPSCALER_HOOK",),
+                    "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                    }
+                }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Upscale"
+
+    @staticmethod
+    def doit(image, segs, model, clip, vae, rescale_factor, resampling_method, supersample, rounding_modulus,
+             seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, feather, inpaint_model, noise_mask_feather,
+             upscale_model_opt=None, upscaler_hook_opt=None, scheduler_func_opt=None):
+
+        new_image = segs_upscaler.upscaler(image, upscale_model_opt, rescale_factor, resampling_method, supersample, rounding_modulus)
+
+        segs = core.segs_scale_match(segs, new_image.shape)
+
+        ordered_segs = segs[1]
+
+        for i, seg in enumerate(ordered_segs):
+            cropped_image = crop_ndarray4(new_image.numpy(), seg.crop_region)
+            cropped_image = to_tensor(cropped_image)
+            mask = to_tensor(seg.cropped_mask)
+            mask = tensor_gaussian_blur_mask(mask, feather)
+
+            is_mask_all_zeros = (seg.cropped_mask == 0).all().item()
+            if is_mask_all_zeros:
+                print(f"SEGSUpscaler: segment skip [empty mask]")
+                continue
+
+            cropped_mask = seg.cropped_mask
+
+            seg_seed = seed + i
+
+            enhanced_image = segs_upscaler.img2img_segs(cropped_image, model, clip, vae, seg_seed, steps, cfg, sampler_name, scheduler,
+                                                        positive, negative, denoise,
+                                                        noise_mask=cropped_mask, control_net_wrapper=seg.control_net_wrapper,
+                                                        inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+            if not (enhanced_image is None):
+                new_image = new_image.cpu()
+                enhanced_image = enhanced_image.cpu()
+                left = seg.crop_region[0]
+                top = seg.crop_region[1]
+                tensor_paste(new_image, enhanced_image, (left, top), mask)
+
+                if upscaler_hook_opt is not None:
+                    new_image = upscaler_hook_opt.post_paste(new_image)
+
+        enhanced_img = tensor_convert_rgb(new_image)
+
+        return (enhanced_img,)
+
+
+class SEGSUpscalerPipe:
+    @classmethod
+    def INPUT_TYPES(s):
+        resampling_methods = ["lanczos", "nearest", "bilinear", "bicubic"]
+
+        return {"required": {
+                    "image": ("IMAGE",),
+                    "segs": ("SEGS",),
+                    "basic_pipe": ("BASIC_PIPE",),
+                    "rescale_factor": ("FLOAT", {"default": 2, "min": 0.01, "max": 100.0, "step": 0.01}),
+                    "resampling_method": (resampling_methods,),
+                    "supersample": (["true", "false"],),
+                    "rounding_modulus": ("INT", {"default": 8, "min": 8, "max": 1024, "step": 8}),
+                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+                    "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
+                    "scheduler": (core.SCHEDULERS,),
+                    "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
+                    "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
+                    "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                    "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
+                    },
+                "optional": {
+                    "upscale_model_opt": ("UPSCALE_MODEL",),
+                    "upscaler_hook_opt": ("UPSCALER_HOOK",),
+                    "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                    }
+                }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Upscale"
+
+    @staticmethod
+    def doit(image, segs, basic_pipe, rescale_factor, resampling_method, supersample, rounding_modulus,
+             seed, steps, cfg, sampler_name, scheduler, denoise, feather, inpaint_model, noise_mask_feather,
+             upscale_model_opt=None, upscaler_hook_opt=None, scheduler_func_opt=None):
+
+        model, clip, vae, positive, negative = basic_pipe
+
+        return SEGSUpscaler.doit(image, segs, model, clip, vae, rescale_factor, resampling_method, supersample, rounding_modulus,
+                                 seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, feather, inpaint_model, noise_mask_feather,
+                                 upscale_model_opt=upscale_model_opt, upscaler_hook_opt=upscaler_hook_opt, scheduler_func_opt=scheduler_func_opt)

modules/impact/segs_upscaler.py

@@ -0,0 +1,137 @@
+from impact.utils import *
+from impact import impact_sampling
+from comfy import model_management
+from comfy.cli_args import args
+import nodes
+
+try:
+    from comfy_extras import nodes_differential_diffusion
+except Exception:
+    print(f"[Impact Pack] ComfyUI is an outdated version. The DifferentialDiffusion feature will be disabled.")
+
+
+# Implementation based on `https://github.com/lingondricka2/Upscaler-Detailer`
+
+# code from comfyroll --->
+# https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/blob/main/nodes/functions_upscale.py
+
+def upscale_with_model(upscale_model, image):
+    device = model_management.get_torch_device()
+    upscale_model.to(device)
+    in_img = image.movedim(-1, -3).to(device)
+    free_memory = model_management.get_free_memory(device)
+
+    tile = 512
+    overlap = 32
+
+    oom = True
+    while oom:
+        try:
+            steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)
+            pbar = comfy.utils.ProgressBar(steps)
+            s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
+            oom = False
+        except model_management.OOM_EXCEPTION as e:
+            tile //= 2
+            if tile < 128:
+                raise e
+
+    s = torch.clamp(s.movedim(-3, -1), min=0, max=1.0)
+    return s
+
+
+def apply_resize_image(image: Image.Image, original_width, original_height, rounding_modulus, mode='scale', supersample='true', factor: int = 2, width: int = 1024, height: int = 1024,
+                       resample='bicubic'):
+    # Calculate the new width and height based on the given mode and parameters
+    if mode == 'rescale':
+        new_width, new_height = int(original_width * factor), int(original_height * factor)
+    else:
+        m = rounding_modulus
+        original_ratio = original_height / original_width
+        height = int(width * original_ratio)
+
+        new_width = width if width % m == 0 else width + (m - width % m)
+        new_height = height if height % m == 0 else height + (m - height % m)
+
+    # Define a dictionary of resampling filters
+    resample_filters = {'nearest': 0, 'bilinear': 2, 'bicubic': 3, 'lanczos': 1}
+
+    # Apply supersample
+    if supersample == 'true':
+        image = image.resize((new_width * 8, new_height * 8), resample=Image.Resampling(resample_filters[resample]))
+
+    # Resize the image using the given resampling filter
+    resized_image = image.resize((new_width, new_height), resample=Image.Resampling(resample_filters[resample]))
+
+    return resized_image
+
+
+def upscaler(image, upscale_model, rescale_factor, resampling_method, supersample, rounding_modulus):
+    if upscale_model is not None:
+        up_image = upscale_with_model(upscale_model, image)
+    else:
+        up_image = image
+
+    pil_img = tensor2pil(image)
+    original_width, original_height = pil_img.size
+    scaled_image = pil2tensor(apply_resize_image(tensor2pil(up_image), original_width, original_height, rounding_modulus, 'rescale',
+                                                 supersample, rescale_factor, 1024, resampling_method))
+    return scaled_image
+
+# <---
+
+
+def img2img_segs(image, model, clip, vae, seed, steps, cfg, sampler_name, scheduler,
+                 positive, negative, denoise, noise_mask, control_net_wrapper=None,
+                 inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+
+    original_image_size = image.shape[1:3]
+
+    # Match to original image size
+    if original_image_size[0] % 8 > 0 or original_image_size[1] % 8 > 0:
+        scale = 8/min(original_image_size[0], original_image_size[1]) + 1
+        w = int(original_image_size[1] * scale)
+        h = int(original_image_size[0] * scale)
+        image = tensor_resize(image, w, h)
+
+    if noise_mask is not None:
+        noise_mask = tensor_gaussian_blur_mask(noise_mask, noise_mask_feather)
+        noise_mask = noise_mask.squeeze(3)
+
+        if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
+            model = nodes_differential_diffusion.DifferentialDiffusion().apply(model)[0]
+
+    if control_net_wrapper is not None:
+        positive, negative, _ = control_net_wrapper.apply(positive, negative, image, noise_mask)
+
+    # 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, image, vae, mask=noise_mask, noise_mask=True)
+        else:
+            print(f"[Impact Pack] ComfyUI is an outdated version.")
+            positive, negative, latent_image = imc_encode(positive, negative, image, vae, noise_mask)
+    else:
+        latent_image = to_latent_image(image, vae)
+        if noise_mask is not None:
+            latent_image['noise_mask'] = noise_mask
+
+    refined_latent = latent_image
+
+    # ksampler
+    refined_latent = impact_sampling.ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, refined_latent, denoise, scheduler_func=scheduler_func_opt)
+
+    # non-latent downscale - latent downscale cause bad quality
+    refined_image = vae.decode(refined_latent['samples'])
+
+    # prevent mixing of device
+    refined_image = refined_image.cpu()
+
+    # Match to original image size
+    if refined_image.shape[1:3] != original_image_size:
+        refined_image = tensor_resize(refined_image, original_image_size[1], original_image_size[0])
+
+    # don't convert to latent - latent break image
+    # preserving pil is much better
+    return refined_image

modules/impact/special_samplers.py

@@ -1,33 +1,37 @@
 import math
 import impact.core as core
+from comfy_extras.nodes_custom_sampler import Noise_RandomNoise
 from impact.utils import *
 from nodes import MAX_RESOLUTION
 import nodes
-from impact.impact_sampling import KSamplerWrapper, KSamplerAdvancedWrapper
-
+from impact.impact_sampling import KSamplerWrapper, KSamplerAdvancedWrapper, separated_sample, impact_sample
+import comfy
 
 class TiledKSamplerProvider:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
-                    "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
-                    "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                    "tile_width": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}),
-                    "tile_height": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}),
-                    "tiling_strategy": (["random", "padded", 'simple'], ),
-                    "basic_pipe": ("BASIC_PIPE", )
+                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "classifier free guidance value"}),
+                    "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler"}),
+                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "noise schedule"}),
+                    "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}),
+                    "tile_width": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64, "tooltip": "Sets the width of the tile to be used in TiledKSampler."}),
+                    "tile_height": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64, "tooltip": "Sets the height of the tile to be used in TiledKSampler."}),
+                    "tiling_strategy": (["random", "padded", 'simple'], {"tooltip": "Sets the tiling strategy for TiledKSampler."} ),
+                    "basic_pipe": ("BASIC_PIPE", {"tooltip": "basic_pipe input for sampling"})
                     }}
 
+    OUTPUT_TOOLTIPS = ("sampler wrapper. (Can be used when generating a regional_prompt.)", )
+
     RETURN_TYPES = ("KSAMPLER",)
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Sampler"
 
-    def doit(self, seed, steps, cfg, sampler_name, scheduler, denoise,
+    @staticmethod
+    def doit(seed, steps, cfg, sampler_name, scheduler, denoise,
              tile_width, tile_height, tiling_strategy, basic_pipe):
         model, _, _, positive, negative = basic_pipe
         sampler = core.TiledKSamplerWrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise,
@@ -39,24 +43,30 @@ class KSamplerProvider:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                                "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
-                                "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                                "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
-                                "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                                "basic_pipe": ("BASIC_PIPE", )
+                                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                                "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "classifier free guidance value"}),
+                                "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler"}),
+                                "scheduler": (core.SCHEDULERS, {"tooltip": "noise schedule"}),
+                                "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}),
+                                "basic_pipe": ("BASIC_PIPE", {"tooltip": "basic_pipe input for sampling"})
                              },
+                "optional": {
+                    "scheduler_func_opt": ("SCHEDULER_FUNC", {"tooltip": "[OPTIONAL] Noise schedule generation function. If this is set, the scheduler widget will be ignored."}),
+                    }
                 }
 
+    OUTPUT_TOOLTIPS = ("sampler wrapper. (Can be used when generating a regional_prompt.)",)
+
     RETURN_TYPES = ("KSAMPLER",)
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Sampler"
 
-    def doit(self, seed, steps, cfg, sampler_name, scheduler, denoise, basic_pipe):
+    @staticmethod
+    def doit(seed, steps, cfg, sampler_name, scheduler, denoise, basic_pipe, scheduler_func_opt=None):
         model, _, _, positive, negative = basic_pipe
-        sampler = KSamplerWrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise)
+        sampler = KSamplerWrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, scheduler_func=scheduler_func_opt)
         return (sampler, )
 
 
@@ -64,25 +74,29 @@ class KSamplerAdvancedProvider:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
-                                "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                                "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
-                                "sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-                                "basic_pipe": ("BASIC_PIPE", )
+                                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "toolip": "classifier free guidance value"}),
+                                "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"toolip": "sampler"}),
+                                "scheduler": (core.SCHEDULERS, {"toolip": "noise schedule"}),
+                                "sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "toolip": "Multiplier of noise schedule"}),
+                                "basic_pipe": ("BASIC_PIPE", {"toolip": "basic_pipe input for sampling"})
                              },
                 "optional": {
-                                "sampler_opt": ("SAMPLER", )
+                                "sampler_opt": ("SAMPLER", {"toolip": "[OPTIONAL] Uses the passed sampler instead of internal impact_sampler."}),
+                                "scheduler_func_opt": ("SCHEDULER_FUNC", {"toolip": "[OPTIONAL] Noise schedule generation function. If this is set, the scheduler widget will be ignored."}),
                             }
                 }
 
+    OUTPUT_TOOLTIPS = ("sampler wrapper. (Can be used when generating a regional_prompt.)", )
+
     RETURN_TYPES = ("KSAMPLER_ADVANCED",)
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Sampler"
 
-    def doit(self, cfg, sampler_name, scheduler, basic_pipe, sigma_factor=1.0, sampler_opt=None):
+    @staticmethod
+    def doit(cfg, sampler_name, scheduler, basic_pipe, sigma_factor=1.0, sampler_opt=None, scheduler_func_opt=None):
         model, _, _, positive, negative = basic_pipe
-        sampler = KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=sampler_opt, sigma_factor=sigma_factor)
+        sampler = KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=sampler_opt, sigma_factor=sigma_factor, scheduler_func=scheduler_func_opt)
         return (sampler, )
 
 
@@ -90,19 +104,22 @@ class TwoSamplersForMask:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "latent_image": ("LATENT", ),
-                     "base_sampler": ("KSAMPLER", ),
-                     "mask_sampler": ("KSAMPLER", ),
-                     "mask": ("MASK", )
+                     "latent_image": ("LATENT", {"tooltip": "input latent image"}),
+                     "base_sampler": ("KSAMPLER", {"tooltip": "Sampler to apply to the region outside the mask."}),
+                     "mask_sampler": ("KSAMPLER", {"tooltip": "Sampler to apply to the masked region."}),
+                     "mask": ("MASK", {"tooltip": "region mask"})
                      },
                 }
 
+    OUTPUT_TOOLTIPS = ("result latent", )
+
     RETURN_TYPES = ("LATENT", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Sampler"
 
-    def doit(self, latent_image, base_sampler, mask_sampler, mask):
+    @staticmethod
+    def doit(latent_image, base_sampler, mask_sampler, mask):
         inv_mask = torch.where(mask != 1.0, torch.tensor(1.0), torch.tensor(0.0))
 
         latent_image['noise_mask'] = inv_mask
@@ -120,82 +137,59 @@ class TwoAdvancedSamplersForMask:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                     "samples": ("LATENT", ),
-                     "base_sampler": ("KSAMPLER_ADVANCED", ),
-                     "mask_sampler": ("KSAMPLER_ADVANCED", ),
-                     "mask": ("MASK", ),
-                     "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000})
+                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}),
+                     "samples": ("LATENT", {"tooltip": "input latent image"}),
+                     "base_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "Sampler to apply to the region outside the mask."}),
+                     "mask_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "Sampler to apply to the masked region."}),
+                     "mask": ("MASK", {"tooltip": "region mask"}),
+                     "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000, "tooltip": "To smooth the seams of the region boundaries, expand the mask by the overlap_factor amount to overlap with other regions."})
                      },
                 }
 
+    OUTPUT_TOOLTIPS = ("result latent", )
+
     RETURN_TYPES = ("LATENT", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Sampler"
 
     @staticmethod
-    def mask_erosion(samples, mask, grow_mask_by):
-        mask = mask.clone()
+    def doit(seed, steps, denoise, samples, base_sampler, mask_sampler, mask, overlap_factor):
+        regional_prompts = RegionalPrompt().doit(mask=mask, advanced_sampler=mask_sampler)[0]
 
-        w = samples['samples'].shape[3]
-        h = samples['samples'].shape[2]
-
-        mask2 = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(w, h), mode="bilinear")
-        if grow_mask_by == 0:
-            mask_erosion = mask2
-        else:
-            kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
-            padding = math.ceil((grow_mask_by - 1) / 2)
-
-            mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask2.round(), kernel_tensor, padding=padding), 0, 1)
-
-        return mask_erosion[:, :, :w, :h].round()
-
-    def doit(self, seed, steps, denoise, samples, base_sampler, mask_sampler, mask, overlap_factor):
-
-        inv_mask = torch.where(mask != 1.0, torch.tensor(1.0), torch.tensor(0.0))
-
-        adv_steps = int(steps / denoise)
-        start_at_step = adv_steps - steps
-
-        new_latent_image = samples.copy()
-
-        mask_erosion = TwoAdvancedSamplersForMask.mask_erosion(samples, mask, overlap_factor)
-
-        for i in range(start_at_step, adv_steps):
-            add_noise = "enable" if i == start_at_step else "disable"
-            return_with_leftover_noise = "enable" if i+1 != adv_steps else "disable"
-
-            new_latent_image['noise_mask'] = inv_mask
-            new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, "enable", recovery_mode="ratio additional")
-
-            new_latent_image['noise_mask'] = mask_erosion
-            new_latent_image = mask_sampler.sample_advanced("disable", seed, adv_steps, new_latent_image, i, i + 1, return_with_leftover_noise, recovery_mode="ratio additional")
-
-        del new_latent_image['noise_mask']
-
-        return (new_latent_image, )
+        return RegionalSampler().doit(seed=seed, seed_2nd=0, seed_2nd_mode="ignore", steps=steps, base_only_steps=1,
+                                      denoise=denoise, samples=samples, base_sampler=base_sampler,
+                                      regional_prompts=regional_prompts, overlap_factor=overlap_factor,
+                                      restore_latent=True, additional_mode="ratio between",
+                                      additional_sampler="AUTO", additional_sigma_ratio=0.3)
 
 
 class RegionalPrompt:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "mask": ("MASK", ),
-                     "advanced_sampler": ("KSAMPLER_ADVANCED", ),
-                     },
+                    "mask": ("MASK", {"tooltip": "region mask"}),
+                    "advanced_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "sampler for specified region"}),
+                    },
+                "optional": {
+                    "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Sets the extra seed to be used for noise variation."}),
+                    "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Sets the strength of the noise variation."}),
+                    "variation_method": (["linear", "slerp"], {"tooltip": "Sets how the original noise and extra noise are blended together."}),
+                    }
                 }
 
+    OUTPUT_TOOLTIPS = ("regional prompts. (Can be used in the RegionalSampler.)", )
+
     RETURN_TYPES = ("REGIONAL_PROMPTS", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Regional"
 
-    def doit(self, mask, advanced_sampler):
-        regional_prompt = core.REGIONAL_PROMPT(mask, advanced_sampler)
+    @staticmethod
+    def doit(mask, advanced_sampler, variation_seed=0, variation_strength=0.0, variation_method="linear"):
+        regional_prompt = core.REGIONAL_PROMPT(mask, advanced_sampler, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method)
         return ([regional_prompt], )
 
 
@@ -203,16 +197,19 @@ class CombineRegionalPrompts:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "regional_prompts1": ("REGIONAL_PROMPTS", ),
+                     "regional_prompts1": ("REGIONAL_PROMPTS", {"tooltip": "input regional_prompts. (Connecting to the input slot increases the number of additional slots.)"}),
                      },
                 }
 
+    OUTPUT_TOOLTIPS = ("Combined REGIONAL_PROMPTS", )
+
     RETURN_TYPES = ("REGIONAL_PROMPTS", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Regional"
 
-    def doit(self, **kwargs):
+    @staticmethod
+    def doit(**kwargs):
         res = []
         for k, v in kwargs.items():
             res += v
@@ -224,16 +221,19 @@ class CombineConditionings:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "conditioning1": ("CONDITIONING", ),
+                     "conditioning1": ("CONDITIONING", { "tooltip": "input conditionings. (Connecting to the input slot increases the number of additional slots.)" }),
                      },
                 }
 
+    OUTPUT_TOOLTIPS = ("Combined conditioning", )
+
     RETURN_TYPES = ("CONDITIONING", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, **kwargs):
+    @staticmethod
+    def doit(**kwargs):
         res = []
         for k, v in kwargs.items():
             res += v
@@ -245,16 +245,19 @@ class ConcatConditionings:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "conditioning1": ("CONDITIONING", ),
+                     "conditioning1": ("CONDITIONING", { "tooltip": "input conditionings. (Connecting to the input slot increases the number of additional slots.)" }),
                      },
                 }
 
+    OUTPUT_TOOLTIPS = ("Concatenated conditioning", )
+
     RETURN_TYPES = ("CONDITIONING", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, **kwargs):
+    @staticmethod
+    def doit(**kwargs):
         conditioning_to = list(kwargs.values())[0]
 
         for k, conditioning_from in list(kwargs.items())[1:]:
@@ -279,29 +282,35 @@ class RegionalSampler:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                     "seed_2nd": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                     "seed_2nd_mode": (["ignore", "fixed", "seed+seed_2nd", "seed-seed_2nd", "increment", "decrement", "randomize"], ),
-                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "base_only_steps": ("INT", {"default": 2, "min": 0, "max": 10000}),
-                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                     "samples": ("LATENT", ),
-                     "base_sampler": ("KSAMPLER_ADVANCED", ),
-                     "regional_prompts": ("REGIONAL_PROMPTS", ),
-                     "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000}),
-                     "restore_latent": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
-                     "additional_mode": (["DISABLE", "ratio additional", "ratio between"], {"default": "ratio between"}),
-                     "additional_sampler": (["AUTO", "euler", "heun", "heunpp2", "dpm_2", "dpm_fast", "dpmpp_2m", "ddpm"],),
-                     "additional_sigma_ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
+                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                     "seed_2nd": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Additional noise seed. The behavior is determined by seed_2nd_mode."}),
+                     "seed_2nd_mode": (["ignore", "fixed", "seed+seed_2nd", "seed-seed_2nd", "increment", "decrement", "randomize"], {"tooltip": "application method of seed_2nd. 1) ignore: Do not use seed_2nd. In the base only sampling stage, the seed is applied as a noise seed, and in the regional sampling stage, denoising is performed as it is without additional noise. 2) Others: In the base only sampling stage, the seed is applied as a noise seed, and once it is closed so that there is no leftover noise, new noise is added with seed_2nd and the regional samping stage is performed. a) fixed: Use seed_2nd as it is as an additional noise seed. b) seed+seed_2nd: Apply the value of seed+seed_2nd as an additional noise seed. c) seed-seed_2nd: Apply the value of seed-seed_2nd as an additional noise seed. d) increment: Not implemented yet. Same with fixed. e) decrement: Not implemented yet. Same with fixed. f) randomize: Not implemented yet. Same with fixed."}),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                     "base_only_steps": ("INT", {"default": 2, "min": 0, "max": 10000, "tooltip": "total sampling steps"}),
+                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}),
+                     "samples": ("LATENT", {"tooltip": "input latent image"}),
+                     "base_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "The sampler applied outside the area set by the regional_prompt."}),
+                     "regional_prompts": ("REGIONAL_PROMPTS", {"tooltip": "The prompt applied to each region"}),
+                     "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000, "tooltip": "To smooth the seams of the region boundaries, expand the mask set in regional_prompts by the overlap_factor amount to overlap with other regions."}),
+                     "restore_latent": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled", "tooltip": "At each step, restore the noise outside the mask area to its original state, as per the principle of inpainting. This option is provided for backward compatibility, and it is recommended to always set it to true."}),
+                     "additional_mode": (["DISABLE", "ratio additional", "ratio between"], {"default": "ratio between", "tooltip": "..._sde or uni_pc and other special samplers are used, the region is not properly denoised, and it causes a phenomenon that destroys the overall harmony. To compensate for this, a recovery operation is performed using another sampler. This requires a longer time for sampling because a second sampling is performed at each step in each region using a special sampler. 1) DISABLE: Disable this feature. 2) ratio additional: After performing the denoise amount to be performed in the step with the sampler set in the region, the recovery sampler is additionally applied by the additional_sigma_ratio. If you use this option, the total denoise amount increases by additional_sigma_ratio. 3) ratio between: The denoise amount to be performed in the step with the sampler set in the region and the denoise amount to be applied to the recovery sampler are divided by additional_sigma_ratio, and denoise is performed for each denoise amount. If you use this option, the total denoise amount does not change."}),
+                     "additional_sampler": (["AUTO", "euler", "heun", "heunpp2", "dpm_2", "dpm_fast", "dpmpp_2m", "ddpm"], {"tooltip": "1) AUTO: Automatically set the recovery sampler. If the sampler is uni_pc, uni_pc_bh2, dpmpp_sde, dpmpp_sde_gpu, the dpm_fast sampler is selected If the sampler is dpmpp_2m_sde, dpmpp_2m_sde_gpu, dpmpp_3m_sde, dpmpp_3m_sde_gpu, the dpmpp_2m sampler is selected. 2) Others: Manually set the recovery sampler."}),
+                     "additional_sigma_ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Multiplier of noise schedule to be applied according to additional_mode."}),
                      },
-                 "hidden": {"unique_id": "UNIQUE_ID"},
+                "hidden": {"unique_id": "UNIQUE_ID"},
                 }
 
+    OUTPUT_TOOLTIPS = ("result latent", )
+
     RETURN_TYPES = ("LATENT", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Regional"
 
+    @staticmethod
+    def separated_sample(*args, **kwargs):
+        return separated_sample(*args, **kwargs)
+
     @staticmethod
     def mask_erosion(samples, mask, grow_mask_by):
         mask = mask.clone()
@@ -320,8 +329,13 @@ class RegionalSampler:
 
         return mask_erosion[:, :, :w, :h].round()
 
-    def doit(self, seed, seed_2nd, seed_2nd_mode, steps, base_only_steps, denoise, samples, base_sampler, regional_prompts, overlap_factor, restore_latent,
+    @staticmethod
+    def doit(seed, seed_2nd, seed_2nd_mode, steps, base_only_steps, denoise, samples, base_sampler, regional_prompts, overlap_factor, restore_latent,
              additional_mode, additional_sampler, additional_sigma_ratio, unique_id=None):
+
+        samples = samples.copy()
+        samples['samples'] = comfy.sample.fix_empty_latent_channels(base_sampler.params[0], samples['samples'])
+
         if restore_latent:
             latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
         else:
@@ -344,7 +358,12 @@ class RegionalSampler:
             if seed_2nd_mode == 'ignore':
                 leftover_noise = True
 
-            samples = base_sampler.sample_advanced(True, seed, adv_steps, samples, start_at_step, start_at_step + base_only_steps, leftover_noise, recovery_mode="DISABLE")
+            noise = Noise_RandomNoise(seed).generate_noise(samples)
+
+            for rp in regional_prompts:
+                noise = rp.touch_noise(noise)
+
+            samples = base_sampler.sample_advanced(True, seed, adv_steps, samples, start_at_step, start_at_step + base_only_steps, leftover_noise, recovery_mode="DISABLE", noise=noise)
 
         if seed_2nd_mode == "seed+seed_2nd":
             seed += seed_2nd
@@ -362,15 +381,21 @@ class RegionalSampler:
 
         if not leftover_noise:
             add_noise = True
+            noise = Noise_RandomNoise(seed).generate_noise(samples)
+
+            for rp in regional_prompts:
+                noise = rp.touch_noise(noise)
         else:
             add_noise = False
+            noise = None
 
         for i in range(start_at_step+base_only_steps, adv_steps):
             core.update_node_status(unique_id, f"{i}/{steps} steps  |         ", ((i-start_at_step)*region_len)/total)
 
             new_latent_image['noise_mask'] = inv_mask
-            new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, True,
-                                                            recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
+            new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image,
+                                                            start_at_step=i, end_at_step=i + 1, return_with_leftover_noise=True,
+                                                            recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio, noise=noise)
 
             if restore_latent:
                 if 'noise_mask' in new_latent_image:
@@ -426,32 +451,38 @@ class RegionalSamplerAdvanced:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "add_noise": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
-                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
-                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
-                     "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000}),
-                     "restore_latent": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
-                     "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
-                     "latent_image": ("LATENT", ),
-                     "base_sampler": ("KSAMPLER_ADVANCED", ),
-                     "regional_prompts": ("REGIONAL_PROMPTS", ),
-                     "additional_mode": (["DISABLE", "ratio additional", "ratio between"], {"default": "ratio between"}),
-                     "additional_sampler": (["AUTO", "euler", "heun", "heunpp2", "dpm_2", "dpm_fast", "dpmpp_2m", "ddpm"],),
-                     "additional_sigma_ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
+                     "add_noise": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled", "tooltip": "Whether to add noise"}),
+                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000, "tooltip": "The starting step of the sampling to be applied at this node within the range of 'steps'."}),
+                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000, "tooltip": "The step at which sampling applied at this node will stop within the range of steps (if greater than steps, sampling will continue only up to steps)."}),
+                     "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000, "tooltip": "To smooth the seams of the region boundaries, expand the mask set in regional_prompts by the overlap_factor amount to overlap with other regions."}),
+                     "restore_latent": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled", "tooltip": "At each step, restore the noise outside the mask area to its original state, as per the principle of inpainting. This option is provided for backward compatibility, and it is recommended to always set it to true."}),
+                     "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled", "tooltip": "Whether to return the latent with noise remaining if the noise has not been completely removed according to the noise schedule, or to completely remove the noise before returning it."}),
+                     "latent_image": ("LATENT", {"tooltip": "input latent image"}),
+                     "base_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "The sampler applied outside the area set by the regional_prompt."}),
+                     "regional_prompts": ("REGIONAL_PROMPTS", {"tooltip": "The prompt applied to each region"}),
+                     "additional_mode": (["DISABLE", "ratio additional", "ratio between"], {"default": "ratio between", "tooltip": "..._sde or uni_pc and other special samplers are used, the region is not properly denoised, and it causes a phenomenon that destroys the overall harmony. To compensate for this, a recovery operation is performed using another sampler. This requires a longer time for sampling because a second sampling is performed at each step in each region using a special sampler. 1) DISABLE: Disable this feature. 2) ratio additional: After performing the denoise amount to be performed in the step with the sampler set in the region, the recovery sampler is additionally applied by the additional_sigma_ratio. If you use this option, the total denoise amount increases by additional_sigma_ratio. 3) ratio between: The denoise amount to be performed in the step with the sampler set in the region and the denoise amount to be applied to the recovery sampler are divided by additional_sigma_ratio, and denoise is performed for each denoise amount. If you use this option, the total denoise amount does not change."}),
+                     "additional_sampler": (["AUTO", "euler", "heun", "heunpp2", "dpm_2", "dpm_fast", "dpmpp_2m", "ddpm"], {"tooltip": "1) AUTO: Automatically set the recovery sampler. If the sampler is uni_pc, uni_pc_bh2, dpmpp_sde, dpmpp_sde_gpu, the dpm_fast sampler is selected If the sampler is dpmpp_2m_sde, dpmpp_2m_sde_gpu, dpmpp_3m_sde, dpmpp_3m_sde_gpu, the dpmpp_2m sampler is selected. 2) Others: Manually set the recovery sampler."}),
+                     "additional_sigma_ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Multiplier of noise schedule to be applied according to additional_mode."}),
                      },
                  "hidden": {"unique_id": "UNIQUE_ID"},
                 }
 
+    OUTPUT_TOOLTIPS = ("result latent", )
+
     RETURN_TYPES = ("LATENT", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Regional"
 
-    def doit(self, add_noise, noise_seed, steps, start_at_step, end_at_step, overlap_factor, restore_latent, return_with_leftover_noise, latent_image, base_sampler, regional_prompts,
+    @staticmethod
+    def doit(add_noise, noise_seed, steps, start_at_step, end_at_step, overlap_factor, restore_latent, return_with_leftover_noise, latent_image, base_sampler, regional_prompts,
              additional_mode, additional_sampler, additional_sigma_ratio, unique_id):
 
+        new_latent_image = latent_image.copy()
+        new_latent_image['samples'] = comfy.sample.fix_empty_latent_channels(base_sampler.params[0], new_latent_image['samples'])
+
         if restore_latent:
             latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
         else:
@@ -467,7 +498,6 @@ class RegionalSamplerAdvanced:
         end_at_step = min(steps, end_at_step)
         total = (end_at_step - start_at_step) * region_len
 
-        new_latent_image = latent_image.copy()
         base_latent_image = None
         region_masks = {}
 
@@ -476,9 +506,16 @@ class RegionalSamplerAdvanced:
 
             cur_add_noise = True if i == start_at_step and add_noise else False
 
+            if cur_add_noise:
+                noise = Noise_RandomNoise(noise_seed).generate_noise(new_latent_image)
+                for rp in regional_prompts:
+                    noise = rp.touch_noise(noise)
+            else:
+                noise = None
+
             new_latent_image['noise_mask'] = inv_mask
             new_latent_image = base_sampler.sample_advanced(cur_add_noise, noise_seed, steps, new_latent_image, i, i + 1, True,
-                                                            recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
+                                                            recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio, noise=noise)
 
             if restore_latent:
                 del new_latent_image['noise_mask']
@@ -535,25 +572,32 @@ class KSamplerBasicPipe:
     @classmethod
     def INPUT_TYPES(s):
         return {"required":
-                    {"basic_pipe": ("BASIC_PIPE",),
-                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
-                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
-                     "latent_image": ("LATENT", ),
-                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                     }
+                    {"basic_pipe": ("BASIC_PIPE", {"tooltip": "basic_pipe input for sampling"}),
+                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "classifier free guidance value"}),
+                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler"}),
+                     "scheduler": (core.SCHEDULERS, {"tooltip": "noise schedule"}),
+                     "latent_image": ("LATENT", {"tooltip": "input latent image"}),
+                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}),
+                     },
+                "optional":
+                    {
+                        "scheduler_func_opt": ("SCHEDULER_FUNC", {"tooltip": "[OPTIONAL] Noise schedule generation function. If this is set, the scheduler widget will be ignored."}),
+                    }
                 }
 
+    OUTPUT_TOOLTIPS = ("passthrough input basic_pipe", "result latent", "VAE in basic_pipe")
+
     RETURN_TYPES = ("BASIC_PIPE", "LATENT", "VAE")
     FUNCTION = "sample"
 
-    CATEGORY = "sampling"
+    CATEGORY = "ImpactPack/sampling"
 
-    def sample(self, basic_pipe, seed, steps, cfg, sampler_name, scheduler, latent_image, denoise=1.0):
+    @staticmethod
+    def sample(basic_pipe, seed, steps, cfg, sampler_name, scheduler, latent_image, denoise=1.0, scheduler_func_opt=None):
         model, clip, vae, positive, negative = basic_pipe
-        latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)[0]
+        latent = impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, scheduler_func=scheduler_func_opt)
         return basic_pipe, latent, vae
 
 
@@ -561,38 +605,79 @@ class KSamplerAdvancedBasicPipe:
     @classmethod
     def INPUT_TYPES(s):
         return {"required":
-                    {"basic_pipe": ("BASIC_PIPE",),
-                     "add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
-                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
-                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
-                     "latent_image": ("LATENT", ),
-                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
-                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
-                     "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
-                     }
+                    {"basic_pipe": ("BASIC_PIPE", {"tooltip": "basic_pipe input for sampling"}),
+                     "add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable", "tooltip": "Whether to add noise"}),
+                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "classifier free guidance value"}),
+                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler"}),
+                     "scheduler": (core.SCHEDULERS, {"tooltip": "noise schedule"}),
+                     "latent_image": ("LATENT", {"tooltip": "input latent image"}),
+                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000, "tooltip": "The starting step of the sampling to be applied at this node within the range of 'steps'."}),
+                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000, "tooltip": "The step at which sampling applied at this node will stop within the range of steps (if greater than steps, sampling will continue only up to steps)."}),
+                     "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable", "tooltip": "Whether to return the latent with noise remaining if the noise has not been completely removed according to the noise schedule, or to completely remove the noise before returning it."}),
+                     },
+                "optional":
+                    {
+                        "scheduler_func_opt": ("SCHEDULER_FUNC", {"tooltip": "[OPTIONAL] Noise schedule generation function. If this is set, the scheduler widget will be ignored."}),
+                    }
                 }
 
+    OUTPUT_TOOLTIPS = ("passthrough input basic_pipe", "result latent", "VAE in basic_pipe")
+
     RETURN_TYPES = ("BASIC_PIPE", "LATENT", "VAE")
     FUNCTION = "sample"
 
-    CATEGORY = "sampling"
+    CATEGORY = "ImpactPack/sampling"
 
-    def sample(self, basic_pipe, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0):
+    @staticmethod
+    def sample(basic_pipe, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0, scheduler_func_opt=None):
         model, clip, vae, positive, negative = basic_pipe
 
-        if add_noise:
-            add_noise = "enable"
-        else:
-            add_noise = "disable"
-
-        if return_with_leftover_noise:
-            return_with_leftover_noise = "enable"
-        else:
-            return_with_leftover_noise = "disable"
-
-        latent = nodes.KSamplerAdvanced().sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise)[0]
+        latent = separated_sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, scheduler_func=scheduler_func_opt)
         return basic_pipe, latent, vae
 
+
+class GITSSchedulerFuncProvider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05, "tooltip": "coeff factor of GITS Scheduler"}),
+                        "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "denoise amount for noise schedule"}),
+                    }
+                }
+
+    OUTPUT_TOOLTIPS = ("Returns a function that generates a noise schedule using GITSScheduler. This can be used in place of a predetermined noise schedule to dynamically generate a noise schedule based on the steps.",)
+
+    RETURN_TYPES = ("SCHEDULER_FUNC",)
+    CATEGORY = "ImpactPack/sampling"
+
+    FUNCTION = "doit"
+
+    @staticmethod
+    def doit(coeff, denoise):
+        def f(model, sampler, steps):
+            if 'GITSScheduler' not in nodes.NODE_CLASS_MAPPINGS:
+                raise Exception("[Impact Pack] ComfyUI is an outdated version. Cannot use GITSScheduler.")
+
+            scheduler = nodes.NODE_CLASS_MAPPINGS['GITSScheduler']()
+            return scheduler.get_sigmas(coeff, steps, denoise)[0]
+
+        return (f, )
+
+
+class NegativeConditioningPlaceholder:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {}}
+
+    OUTPUT_TOOLTIPS = ("This is a Placeholder for the FLUX model that does not use Negative Conditioning.",)
+
+    RETURN_TYPES = ("CONDITIONING",)
+    CATEGORY = "ImpactPack/sampling"
+
+    FUNCTION = "doit"
+
+    @staticmethod
+    def doit():
+        return ("NegativePlaceholder", )

modules/impact/util_nodes.py

@@ -5,50 +5,85 @@ import torch
 import comfy
 import sys
 import nodes
+import re
+import impact.core as core
+from server import PromptServer
+import inspect
 
 
 class GeneralSwitch:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {
-                    "select": ("INT", {"default": 1, "min": 1, "max": 999999, "step": 1}),
-                    "sel_mode": ("BOOLEAN", {"default": True, "label_on": "select_on_prompt", "label_off": "select_on_execution", "forceInput": False}),
-                    },
-                "optional": {
-                    "input1": (any_typ,),
+        dyn_inputs = {"input1": (any_typ, {"lazy": True, "tooltip": "Any input. When connected, one more input slot is added."}), }
+        if core.is_execution_model_version_supported():
+            stack = inspect.stack()
+            if stack[2].function == 'get_input_info':
+                # bypass validation
+                class AllContainer:
+                    def __contains__(self, item):
+                        return True
+
+                    def __getitem__(self, key):
+                        return any_typ, {"lazy": True}
+
+                dyn_inputs = AllContainer()
+
+        inputs = {"required": {
+                    "select": ("INT", {"default": 1, "min": 1, "max": 999999, "step": 1, "tooltip": "The input number you want to output among the inputs"}),
+                    "sel_mode": ("BOOLEAN", {"default": False, "label_on": "select_on_prompt", "label_off": "select_on_execution", "forceInput": False,
+                                             "tooltip": "In the case of 'select_on_execution', the selection is dynamically determined at the time of workflow execution. 'select_on_prompt' is an option that exists for older versions of ComfyUI, and it makes the decision before the workflow execution."}),
                     },
+                "optional": dyn_inputs,
                 "hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO"}
                 }
 
+        return inputs
+
     RETURN_TYPES = (any_typ, "STRING", "INT")
     RETURN_NAMES = ("selected_value", "selected_label", "selected_index")
+    OUTPUT_TOOLTIPS = ("Output is generated only from the input chosen by the 'select' value.", "Slot label of the selected input slot", "Outputs the select value as is")
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, *args, **kwargs):
+    def check_lazy_status(self, *args, **kwargs):
+        selected_index = int(kwargs['select'])
+        input_name = f"input{selected_index}"
+
+        print(f"SELECTED: {input_name}")
+
+        if input_name in kwargs:
+            return [input_name]
+        else:
+            return []
+
+    @staticmethod
+    def doit(*args, **kwargs):
         selected_index = int(kwargs['select'])
         input_name = f"input{selected_index}"
 
         selected_label = input_name
         node_id = kwargs['unique_id']
-        nodelist = kwargs['extra_pnginfo']['workflow']['nodes']
-        for node in nodelist:
-            if str(node['id']) == node_id:
-                inputs = node['inputs']
 
-                for slot in inputs:
-                    if slot['name'] == input_name and 'label' in slot:
-                        selected_label = slot['label']
+        if 'extra_pnginfo' in kwargs and kwargs['extra_pnginfo'] is not None:
+            nodelist = kwargs['extra_pnginfo']['workflow']['nodes']
+            for node in nodelist:
+                if str(node['id']) == node_id:
+                    inputs = node['inputs']
 
-                break
+                    for slot in inputs:
+                        if slot['name'] == input_name and 'label' in slot:
+                            selected_label = slot['label']
+
+                    break
+        else:
+            print(f"[Impact-Pack] The switch node does not guarantee proper functioning in API mode.")
 
         if input_name in kwargs:
-            return (kwargs[input_name], selected_label, selected_index)
+            return kwargs[input_name], selected_label, selected_index
         else:
             print(f"ImpactSwitch: invalid select index (ignored)")
-            return (None, "", selected_index)
-
+            return None, "", selected_index
 
 class LatentSwitch:
     @classmethod
@@ -120,23 +155,44 @@ class GeneralInversedSwitch:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                    "select": ("INT", {"default": 1, "min": 1, "max": 999999, "step": 1}),
-                    "input": (any_typ,),
+                    "select": ("INT", {"default": 1, "min": 1, "max": 999999, "step": 1, "tooltip": "The output number you want to send from the input"}),
+                    "input": (any_typ, {"tooltip": "Any input. When connected, one more input slot is added."}),
+
                     },
-                "hidden": {"unique_id": "UNIQUE_ID"},
+                "optional": {
+                    "sel_mode": ("BOOLEAN", {"default": False, "label_on": "select_on_prompt", "label_off": "select_on_execution", "forceInput": False,
+                                             "tooltip": "In the case of 'select_on_execution', the selection is dynamically determined at the time of workflow execution. 'select_on_prompt' is an option that exists for older versions of ComfyUI, and it makes the decision before the workflow execution."}),
+                    },
+                "hidden": {"prompt": "PROMPT", "unique_id": "UNIQUE_ID"},
                 }
 
     RETURN_TYPES = ByPassTypeTuple((any_typ, ))
+    OUTPUT_TOOLTIPS = ("Output occurs only from the output selected by the 'select' value.\nWhen slots are connected, additional slots are created.", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, select, input, unique_id):
+    def doit(self, select, prompt, unique_id, input, **kwargs):
+        if core.is_execution_model_version_supported():
+            from comfy_execution.graph import ExecutionBlocker
+        else:
+            print("[Impact Pack] InversedSwitch: ComfyUI is outdated. The 'select_on_execution' mode cannot function properly.")
+
         res = []
 
-        for i in range(0, select):
+        # search max output count in prompt
+        cnt = 0
+        for x in prompt.values():
+            for y in x.get('inputs', {}).values():
+                if isinstance(y, list) and len(y) == 2:
+                    if y[0] == unique_id:
+                        cnt = max(cnt, y[1])
+
+        for i in range(0, cnt + 1):
             if select == i+1:
                 res.append(input)
+            elif core.is_execution_model_version_supported():
+                res.append(ExecutionBlocker(None))
             else:
                 res.append(None)
 
@@ -190,9 +246,10 @@ class ImpactLogger:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                        "data": (any_typ, ""),
+                        "data": (any_typ,),
+                        "text": ("STRING", {"multiline": True}),
                     },
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"},
                 }
 
     CATEGORY = "ImpactPack/Debug"
@@ -202,7 +259,7 @@ class ImpactLogger:
     RETURN_TYPES = ()
     FUNCTION = "doit"
 
-    def doit(self, data, prompt, extra_pnginfo):
+    def doit(self, data, text, prompt, extra_pnginfo, unique_id):
         shape = ""
         if hasattr(data, "shape"):
             shape = f"{data.shape} / "
@@ -213,12 +270,13 @@ class ImpactLogger:
 
         # for x in prompt:
         #     if 'inputs' in x and 'populated_text' in x['inputs']:
-        #         print(f"PROMP: {x['10']['inputs']['populated_text']}")
+        #         print(f"PROMPT: {x['10']['inputs']['populated_text']}")
         #
         # for x in extra_pnginfo['workflow']['nodes']:
         #     if x['type'] == 'ImpactWildcardProcessor':
         #         print(f" WV : {x['widgets_values'][1]}\n")
 
+        PromptServer.instance.send_sync("impact-node-feedback", {"node_id": unique_id, "widget_name": "text", "type": "TEXT", "value": f"{data}"})
         return {}
 
 
@@ -318,7 +376,7 @@ class ImageListToImageBatch:
 
     def doit(self, images):
         if len(images) <= 1:
-            return (images,)
+            return (images[0],)
         else:
             image1 = images[0]
             for image2 in images[1:]:
@@ -344,6 +402,50 @@ class ImageBatchToImageList:
         return (images, )
 
 
+class MakeAnyList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {},
+            "optional": {"value1": (any_typ,), }
+        }
+
+    RETURN_TYPES = (any_typ,)
+    OUTPUT_IS_LIST = (True,)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, **kwargs):
+        values = []
+
+        for k, v in kwargs.items():
+            if v is not None:
+                values.append(v)
+
+        return (values, )
+
+
+class MakeMaskList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"mask1": ("MASK",), }}
+
+    RETURN_TYPES = ("MASK",)
+    OUTPUT_IS_LIST = (True,)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, **kwargs):
+        masks = []
+
+        for k, v in kwargs.items():
+            masks.append(v)
+
+        return (masks, )
+
+
 class MakeImageList:
     @classmethod
     def INPUT_TYPES(s):
@@ -389,6 +491,31 @@ class MakeImageBatch:
             return (image1,)
 
 
+class MakeMaskBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"mask1": ("MASK",), }}
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, **kwargs):
+        mask1 = kwargs['mask1']
+        del kwargs['mask1']
+        masks = [make_3d_mask(value) for value in kwargs.values()]
+
+        if len(masks) == 0:
+            return (mask1,)
+        else:
+            for mask2 in masks:
+                if mask1.shape[1:] != mask2.shape[1:]:
+                    mask2 = comfy.utils.common_upscale(mask2.movedim(-1, 1), mask1.shape[2], mask1.shape[1], "lanczos", "center").movedim(1, -1)
+                mask1 = torch.cat((mask1, mask2), dim=0)
+            return (mask1,)
+
+
 class ReencodeLatent:
     @classmethod
     def INPUT_TYPES(s):
@@ -485,3 +612,94 @@ class StringSelector:
                 selected = lines[select % len(lines)]
 
         return (selected, )
+
+
+class StringListToString:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "join_with": ("STRING", {"default": "\\n"}),
+                "string_list": ("STRING", {"forceInput": True}),
+            }
+        }
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, join_with, string_list):
+        # convert \\n to newline character
+        if join_with[0] == "\\n":
+            join_with[0] = "\n"
+
+        joined_text = join_with[0].join(string_list)
+
+        return (joined_text,)
+
+
+class WildcardPromptFromString:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": ("STRING", {"forceInput": True}),
+                "delimiter": ("STRING", {"multiline": False, "default": "\\n" }),
+                "prefix_all": ("STRING", {"multiline": False}),
+                "postfix_all": ("STRING", {"multiline": False}),
+                "restrict_to_tags": ("STRING", {"multiline": False}),
+                "exclude_tags": ("STRING", {"multiline": False})
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "STRING",)
+    RETURN_NAMES = ("wildcard", "segs_labels",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, string, delimiter, prefix_all, postfix_all, restrict_to_tags, exclude_tags):
+        # convert \\n to newline character
+        if delimiter == "\\n":
+            delimiter = "\n"
+
+        # some sanity checks and normalization for later processing
+        if prefix_all is None:
+            prefix_all = ""
+        if postfix_all is None:
+            postfix_all = ""
+        if restrict_to_tags is None:
+            restrict_to_tags = ""
+        if exclude_tags is None:
+            exclude_tags = ""
+
+        restrict_to_tags = restrict_to_tags.split(", ")
+        exclude_tags = exclude_tags.split(", ")
+
+        # build the wildcard prompt per list entry
+        output = ["[LAB]"]
+        labels = []
+        for x in string.split(delimiter):
+            label = str(len(labels) + 1)
+            labels.append(label)
+            x = x.split(", ")
+            # restrict to tags
+            if restrict_to_tags != [""]:
+                x = list(set(x) & set(restrict_to_tags))
+            # remove tags
+            if exclude_tags != [""]:
+                x = list(set(x) - set(exclude_tags))
+            # next row: <LABEL> <PREFIX> <TAGS> <POSTFIX>
+            prompt_for_seg = f'[{label}] {prefix_all} {", ".join(x)} {postfix_all}'.strip()
+            output.append(prompt_for_seg)
+        output = "\n".join(output)
+
+        # clean string: fixup double spaces, commas etc.
+        output = re.sub(r' ,', ',', output)
+        output = re.sub(r'  +', ' ', output)
+        output = re.sub(r',,+', ',', output)
+        output = re.sub(r'\n, ', '\n', output)
+
+        return output, ", ".join(labels)

modules/impact/utils.py

@@ -5,11 +5,8 @@ import numpy as np
 import folder_paths
 import nodes
 from . import config
-from PIL import Image, ImageFilter
-from scipy.ndimage import zoom
+from PIL import Image
 import comfy
-import comfy.ldm.cascade as cascade
-from comfy_extras import nodes_stable_cascade
 
 
 class TensorBatchBuilder:
@@ -128,7 +125,7 @@ def to_pil(image):
 
 def to_tensor(image):
     if isinstance(image, Image.Image):
-        return torch.from_numpy(np.array(image))
+        return torch.from_numpy(np.array(image)) / 255.0
     if isinstance(image, torch.Tensor):
         return image
     if isinstance(image, np.ndarray):
@@ -185,7 +182,8 @@ def tensor_paste(image1, image2, left_top, mask):
     _tensor_check_image(image2)
     _tensor_check_mask(mask)
     if image2.shape[1:3] != mask.shape[1:3]:
-        raise ValueError(f"Inconsistent size: Image ({image2.shape[1:3]}) != Mask ({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
@@ -477,6 +475,17 @@ def crop_ndarray4(npimg, crop_region):
 crop_tensor4 = crop_ndarray4
 
 
+def crop_ndarray3(npimg, crop_region):
+    x1 = crop_region[0]
+    y1 = crop_region[1]
+    x2 = crop_region[2]
+    y2 = crop_region[3]
+
+    cropped = npimg[:, y1:y2, x1:x2]
+
+    return cropped
+
+
 def crop_ndarray2(npimg, crop_region):
     x1 = crop_region[0]
     y1 = crop_region[1]
@@ -492,19 +501,13 @@ def crop_image(image, crop_region):
     return crop_tensor4(image, crop_region)
 
 
-def to_latent_image(pixels, vae, compression=None):
+def to_latent_image(pixels, vae):
     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()
-    if hasattr(nodes.VAEEncode, "vae_encode_crop_pixels"):
-        # backward compatibility
-        print(f"[Impact Pack] ComfyUI is outdated.")
-        pixels = vae_encode.vae_encode_crop_pixels(pixels)
-        t = vae.encode(pixels[:, :, :, :3])
-        return {"samples": t}
 
     return vae_encode.encode(vae, pixels)[0]
 
@@ -533,6 +536,16 @@ def make_3d_mask(mask):
     return mask
 
 
+def make_4d_mask(mask):
+    if len(mask.shape) == 3:
+        return mask.unsqueeze(0)
+
+    elif len(mask.shape) == 2:
+        return mask.unsqueeze(0).unsqueeze(0)
+
+    return mask
+
+
 def is_same_device(a, b):
     a_device = torch.device(a) if isinstance(a, str) else a
     b_device = torch.device(b) if isinstance(b, str) else b
@@ -552,7 +565,8 @@ from torchvision.transforms.functional import to_pil_image
 
 
 def resize_mask(mask, size):
-    resized_mask = torch.nn.functional.interpolate(mask.unsqueeze(0), size=size, mode='bilinear', align_corners=False)
+    mask = make_4d_mask(mask)
+    resized_mask = torch.nn.functional.interpolate(mask, size=size, mode='bilinear', align_corners=False)
     return resized_mask.squeeze(0)
 
 
@@ -564,6 +578,14 @@ def apply_mask_alpha_to_pil(decoded_pil, mask):
     return decoded_rgba
 
 
+def flatten_mask(all_masks):
+    merged_mask = (all_masks[0] * 255).to(torch.uint8)
+    for mask in all_masks[1:]:
+        merged_mask |= (mask * 255).to(torch.uint8)
+
+    return merged_mask
+
+
 def try_install_custom_node(custom_node_url, msg):
     try:
         import cm_global

modules/impact/wildcards.py

@@ -7,8 +7,12 @@ import yaml
 import numpy as np
 import threading
 from impact import utils
+from impact import config
 
 
+wildcards_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "wildcards"))
+
+RE_WildCardQuantifier = re.compile(r"(?P<quantifier>\d+)#__(?P<keyword>[\w.\-+/*\\]+?)__", re.IGNORECASE)
 wildcard_lock = threading.Lock()
 wildcard_dict = {}
 
@@ -25,7 +29,7 @@ def get_wildcard_dict():
 
 
 def wildcard_normalize(x):
-    return x.replace("\\", "/").lower()
+    return x.replace("\\", "/").replace(' ', '-').lower()
 
 
 def read_wildcard(k, v):
@@ -37,6 +41,9 @@ def read_wildcard(k, v):
             new_key = f"{k}/{k2}"
             new_key = wildcard_normalize(new_key)
             read_wildcard(new_key, v2)
+    elif isinstance(v, str):
+        k = wildcard_normalize(k)
+        wildcard_dict[k] = [v]
 
 
 def read_wildcard_dict(wildcard_path):
@@ -46,32 +53,62 @@ def read_wildcard_dict(wildcard_path):
             if file.endswith('.txt'):
                 file_path = os.path.join(root, file)
                 rel_path = os.path.relpath(file_path, wildcard_path)
-                key = os.path.splitext(rel_path)[0].replace('\\', '/').lower()
+                key = wildcard_normalize(os.path.splitext(rel_path)[0])
 
                 try:
                     with open(file_path, 'r', encoding="ISO-8859-1") as f:
                         lines = f.read().splitlines()
-                        wildcard_dict[key] = lines
-                except UnicodeDecodeError:
+                        wildcard_dict[key] = [x for x in lines if not x.strip().startswith('#')]
+                except yaml.reader.ReaderError:
                     with open(file_path, 'r', encoding="UTF-8", errors="ignore") as f:
                         lines = f.read().splitlines()
-                        wildcard_dict[key] = lines
+                        wildcard_dict[key] = [x for x in lines if not x.strip().startswith('#')]
             elif file.endswith('.yaml'):
                 file_path = os.path.join(root, file)
-                with open(file_path, 'r') as f:
-                    yaml_data = yaml.load(f, Loader=yaml.FullLoader)
 
-                    for k, v in yaml_data.items():
-                        read_wildcard(k, v)
+                try:
+                    with open(file_path, 'r', encoding="ISO-8859-1") as f:
+                        yaml_data = yaml.load(f, Loader=yaml.FullLoader)
+                except yaml.reader.ReaderError as e:
+                    with open(file_path, 'r', encoding="UTF-8", errors="ignore") as f:
+                        yaml_data = yaml.load(f, Loader=yaml.FullLoader)
+
+                for k, v in yaml_data.items():
+                    read_wildcard(k, v)
 
     return wildcard_dict
 
 
+def process_comment_out(text):
+    lines = text.split('\n')
+
+    lines0 = []
+    flag = False
+    for line in lines:
+        if line.lstrip().startswith('#'):
+            flag = True
+            continue
+
+        if len(lines0) == 0:
+            lines0.append(line)
+        elif flag:
+            lines0[-1] += ' ' + line
+            flag = False
+        else:
+            lines0.append(line)
+
+    return '\n'.join(lines0)
+
+
 def process(text, seed=None):
+    text = process_comment_out(text)
+
     if seed is not None:
         random.seed(seed)
     random_gen = np.random.default_rng(seed)
 
+    local_wildcard_dict = get_wildcard_dict()
+
     def replace_options(string):
         replacements_found = False
 
@@ -84,6 +121,7 @@ def process(text, seed=None):
             select_sep = ' '
             range_pattern = r'(\d+)(-(\d+))?'
             range_pattern2 = r'-(\d+)'
+            wildcard_pattern = r"__([\w.\-+/*\\]+?)__"
 
             if len(multi_select_pattern) > 1:
                 r = re.match(range_pattern, options[0])
@@ -109,7 +147,13 @@ def process(text, seed=None):
 
                     if select_range is not None and len(multi_select_pattern) == 2:
                         # PATTERN: count$$
-                        options[0] = multi_select_pattern[1]
+                        matches = re.findall(wildcard_pattern, multi_select_pattern[1])
+                        if len(options) == 1 and matches:
+                            # count$$<single wildcard>
+                            options = get_wildcard_options(multi_select_pattern[1])
+                        else:
+                            # count$$opt1|opt2|...
+                            options[0] = multi_select_pattern[1]
                     elif select_range is not None and len(multi_select_pattern) == 3:
                         # PATTERN: count$$ sep $$
                         select_sep = multi_select_pattern[1]
@@ -155,9 +199,36 @@ def process(text, seed=None):
 
         return replaced_string, replacements_found
 
+    def get_wildcard_options(string):
+        pattern = r"__([\w.\-+/*\\]+?)__"
+        matches = re.findall(pattern, string)
+
+        options = []
+
+        for match in matches:
+            keyword = match.lower()
+            keyword = wildcard_normalize(keyword)
+            if keyword in local_wildcard_dict:
+                options.extend(local_wildcard_dict[keyword])
+            elif '*' in keyword:
+                subpattern = keyword.replace('*', '.*').replace('+', '\\+')
+                total_patterns = []
+                found = False
+                for k, v in local_wildcard_dict.items():
+                    if re.match(subpattern, k) is not None or re.match(subpattern, k+'/') is not None:
+                        total_patterns += v
+                        found = True
+
+                if found:
+                    options.extend(total_patterns)
+            elif '/' not in keyword:
+                string_fallback = string.replace(f"__{match}__", f"__*/{match}__", 1)
+                options.extend(get_wildcard_options(string_fallback))
+
+        return options
+
     def replace_wildcard(string):
-        local_wildcard_dict = get_wildcard_dict()
-        pattern = r"__([\w.\-+/*\\]+)__"
+        pattern = r"__([\w.\-+/*\\]+?)__"
         matches = re.findall(pattern, string)
 
         replacements_found = False
@@ -170,11 +241,11 @@ def process(text, seed=None):
                 replacements_found = True
                 string = string.replace(f"__{match}__", replacement, 1)
             elif '*' in keyword:
-                subpattern = keyword.replace('*', '.*').replace('+','\+')
+                subpattern = keyword.replace('*', '.*').replace('+', '\\+')
                 total_patterns = []
                 found = False
                 for k, v in local_wildcard_dict.items():
-                    if re.match(subpattern, k) is not None:
+                    if re.match(subpattern, k) is not None or re.match(subpattern, k+'/') is not None:
                         total_patterns += v
                         found = True
 
@@ -192,6 +263,15 @@ def process(text, seed=None):
     stop_unwrap = False
     while not stop_unwrap and replace_depth > 1:
         replace_depth -= 1  # prevent infinite loop
+        
+        option_quantifier = [e.groupdict() for e in RE_WildCardQuantifier.finditer(text)]
+        for match in option_quantifier:
+            keyword = match['keyword'].lower()
+            quantifier = int(match['quantifier']) if match['quantifier'] else 1
+            replacement = '__|__'.join([keyword,] * quantifier)
+            wilder_keyword = keyword.replace('*', '\\*')
+            RE_TEMP = re.compile(fr"(?P<quantifier>\d+)#__(?P<keyword>{wilder_keyword})__", re.IGNORECASE)
+            text = RE_TEMP.sub(f"__{replacement}__", text)
 
         # pass1: replace options
         pass1, is_replaced1 = replace_options(text)
@@ -207,7 +287,7 @@ def process(text, seed=None):
 
 
 def is_numeric_string(input_str):
-    return re.match(r'^-?\d+(\.\d+)?$', input_str) is not None
+    return re.match(r'^-?(\d*\.?\d+|\d+\.?\d*)$', input_str) is not None
 
 
 def safe_float(x):
@@ -287,10 +367,22 @@ def resolve_lora_name(lora_name_cache, name):
                 return x
 
 
-def process_with_loras(wildcard_opt, model, clip, clip_encoder=None):
+def process_with_loras(wildcard_opt, model, clip, clip_encoder=None, seed=None, processed=None):
+    """
+    process wildcard text including loras
+
+    :param wildcard_opt: wildcard text
+    :param model: model
+    :param clip: clip
+    :param clip_encoder: you can pass custom encoder such as adv_cliptext_encode
+    :param seed: seed for populating
+    :param processed: output variable - [pass1, pass2, pass3] will be saved into passed list
+    :return: model, clip, conditioning
+    """
+
     lora_name_cache = []
 
-    pass1 = process(wildcard_opt)
+    pass1 = process(wildcard_opt, seed)
     loras = extract_lora_values(pass1)
     pass2 = remove_lora_tags(pass1)
 
@@ -351,12 +443,17 @@ def process_with_loras(wildcard_opt, model, clip, clip_encoder=None):
         else:
             result = cur
 
+    if processed is not None:
+        processed.append(pass1)
+        processed.append(pass2)
+        processed.append(pass3)
+
     return model, clip, result
 
 
 def starts_with_regex(pattern, text):
     regex = re.compile(pattern)
-    return bool(regex.match(text))
+    return regex.match(text)
 
 
 def split_to_dict(text):
@@ -438,15 +535,33 @@ def process_wildcard_for_segs(wildcard):
 
         return 'LAB', WildcardChooserDict(items)
 
-    elif starts_with_regex(r"\[(ASC|DSC|RND)\]", wildcard):
-        mode = wildcard[1:4]
-        items = split_string_with_sep(wildcard[5:])
-
-        if mode == 'RND':
-            random.shuffle(items)
-            return mode, WildcardChooser(items, True)
-        else:
-            return mode, WildcardChooser(items, False)
-
     else:
-        return None, WildcardChooser([(None, wildcard)], False)
+        match = starts_with_regex(r"\[(ASC-SIZE|DSC-SIZE|ASC|DSC|RND)\]", wildcard)
+
+        if match:
+            mode = match[1]
+            items = split_string_with_sep(wildcard[len(match[0]):])
+
+            if mode == 'RND':
+                random.shuffle(items)
+                return mode, WildcardChooser(items, True)
+            else:
+                return mode, WildcardChooser(items, False)
+
+        else:
+            return None, WildcardChooser([(None, wildcard)], False)
+
+
+def wildcard_load():
+    global wildcard_dict
+    wildcard_dict = {}
+
+    with wildcard_lock:
+        read_wildcard_dict(wildcards_path)
+
+        try:
+            read_wildcard_dict(config.get_config()['custom_wildcards'])
+        except Exception as e:
+            print(f"[Impact Pack] Failed to load custom wildcards directory.")
+
+        print(f"[Impact Pack] Wildcards loading done.")

modules/thirdparty/noise_nodes.py

@@ -5,6 +5,9 @@
 import comfy
 import torch
 
+from comfy import sampler_helpers
+
+
 class Unsampler:
     @classmethod
     def INPUT_TYPES(s):
@@ -39,25 +42,27 @@ class Unsampler:
         noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
         noise_mask = None
         if "noise_mask" in latent:
-            noise_mask = comfy.sample.prepare_mask(latent["noise_mask"], noise.shape, device)
-
-        real_model = None
-        real_model = model.model
+            noise_mask = comfy.sampler_helpers.prepare_mask(latent["noise_mask"], noise.shape, device)
 
         noise = noise.to(device)
         latent_image = latent_image.to(device)
 
-        positive = comfy.sample.convert_cond(positive)
-        negative = comfy.sample.convert_cond(negative)
+        conds0 = \
+            {"positive": comfy.sampler_helpers.convert_cond(positive),
+             "negative": comfy.sampler_helpers.convert_cond(negative)}
 
-        models, inference_memory = comfy.sample.get_additional_models(positive, negative, model.model_dtype())
+        conds = {}
+        for k in conds0:
+            conds[k] = list(map(lambda a: a.copy(), conds0[k]))
+
+        models, inference_memory = comfy.sampler_helpers.get_additional_models(conds, model.model_dtype())
 
         comfy.model_management.load_models_gpu([model] + models, model.memory_required(noise.shape) + inference_memory)
 
-        sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name,
+        sampler = comfy.samplers.KSampler(model, steps=steps, device=device, sampler=sampler_name,
                                           scheduler=scheduler, denoise=1.0, model_options=model.model_options)
 
-        sigmas = sigmas = sampler.sigmas.flip(0) + 0.0001
+        sigmas = sampler.sigmas.flip(0) + 0.0001
 
         pbar = comfy.utils.ProgressBar(steps)
 
@@ -73,8 +78,9 @@ class Unsampler:
             samples /= samples.std()
         samples = samples.cpu()
 
-        comfy.sample.cleanup_additional_models(models)
+        comfy.sampler_helpers.cleanup_additional_models(models)
 
         out = latent.copy()
         out["samples"] = samples
         return (out,)
+

pyproject.toml

@@ -0,0 +1,15 @@
+[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 = "8.1.1"
+license = { file = "LICENSE.txt" }
+dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]
+
+[project.urls]
+Repository = "https://github.com/ltdrdata/ComfyUI-Impact-Pack"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "drltdata"
+DisplayName = "ComfyUI Impact Pack"
+Icon = ""

requirements.txt

@@ -3,4 +3,7 @@ scikit-image
 piexif
 transformers
 opencv-python-headless
-GitPython
+scipy>=1.11.4
+numpy<2
+dill
+matplotlib