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Add custom nodes, Civitai loras (LFS), and vast.ai setup script
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Browse Source 
Includes 30 custom nodes committed directly, 7 Civitai-exclusive
loras stored via Git LFS, and a setup script that installs all
dependencies and downloads HuggingFace-hosted models on vast.ai.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
jaidaken
2026-02-09 00:55:26 +00:00
parent 2b70ab9ad0
commit f09734b0ee
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__pycache__
*.ini
wildcards/**
.vscode/
.idea/
subpack
impact_subpack
*.txt
*.yaml
!requirements.txt
!LICENSE.txt
.claude/

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# ComfyUI-Impact-Pack
**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
* V8.24: This compatibility patch requires ComfyUI version 0.3.63 or higher due to structural changes in DifferentialDiffusion.
* V8.19: legacy nodes (mmdet and etc.) are removed
* V8.18: Support [facebookresearch/sam2](https://github.com/facebookresearch/sam2) models
* V8.0: The `Impact Subpack` is no longer installed automatically. To use `UltralyticsDetectorProvider` nodes, please install the `Impact Subpack` separately.
* V7.6: Automatic installation is no longer supported. Please install using ComfyUI-Manager, or manually install requirements.txt and run install.py to complete the installation.
* V7.0: Supports Switch based on Execution Model Inversion.
* V6.0: Supports FLUX.1 model in Impact KSampler, Detailers, PreviewBridgeLatent
* V5.0: It is no longer compatible with versions of ComfyUI before 2024.04.08.
* 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* V4.85: Incompatible with the outdated **ComfyUI IPAdapter Plus**.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.
* V4.12: `MASKS` is changed to `MASK`.
* V4.7.2 isn't compatible with old version of `ControlNet Auxiliary Preprocessor`. If you will use `MediaPipe FaceMesh to SEGS` update to latest version(Sep. 17th).
* Selection weight syntax is changed(: -> ::) since V3.16. ([tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcardProcessor.md))
* Starting from V3.6, requires latest version(Aug 8, 9ccc965) of ComfyUI.
* **In versions below V3.3.1, there was an issue with the image quality generated after using the UltralyticsDetectorProvider. Please make sure to upgrade to a newer version.**
* Starting from V3.0, nodes related to `mmdet` are optional nodes that are activated only based on the configuration settings.
- Through ComfyUI-Impact-Subpack, you can utilize UltralyticsDetectorProvider to access various detection models.
* Between versions 2.22 and 2.21, there is partial compatibility loss regarding the Detailer workflow. If you continue to use the existing workflow, errors may occur during execution. An additional output called "enhanced_alpha_list" has been added to Detailer-related nodes.
* The permission error related to cv2 that occurred during the installation of Impact Pack has been patched in version 2.21.4. However, please note that the latest versions of ComfyUI and ComfyUI-Manager are required.
* The "PreviewBridge" feature may not function correctly on ComfyUI versions released before July 1, 2023.
* Attempting to load the "ComfyUI-Impact-Pack" on ComfyUI versions released before June 27, 2023, will result in a failure.
* With the addition of wildcard support in FaceDetailer, the structure of DETAILER_PIPE-related nodes and Detailer nodes has changed. There may be malfunctions when using the existing workflow.
## How To Install
### **Recommended**
* Install via [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager).
### **Manual**
* Navigate to `ComfyUI/custom_nodes` in your terminal (cmd).
* Clone the repository under the `custom_nodes` directory using the following command:
```
git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack comfyui-impact-pack
cd comfyui-impact-pack
```
* Install dependencies in your Python environment.
* For Windows Portable, run the following command inside `ComfyUI\custom_nodes\comfyui-impact-pack`:
```
..\..\..\python_embeded\python.exe -m pip install -r requirements.txt
```
* If using venv or conda, activate your Python environment first, then run:
```
pip install -r requirements.txt
```
### Companion Pack
* If you need the `Ultralytics Detector Provider` to use various YOLO detection models, you should also install [ComfyUI-Impact-Subpack](https://github.com/ltdrdata/ComfyUI-Impact-Subpack).
## Custom Nodes
### [Detector nodes](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/detectors.md)
* `SAMLoader (Impact)` - Loads the SAM model.
* `ONNXDetectorProvider` - Loads the ONNX model to provide BBOX_DETECTOR.
* `CLIPSegDetectorProvider` - Wrapper for CLIPSeg to provide BBOX_DETECTOR.
* You need to install the ComfyUI-CLIPSeg node extension.
* `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 for Video (SEGS)` – Performs detection on videos composed of image frames. Instead of using a single mask, it performs detection individually on each image frame and generates a SEGS object with a batch of masks.
* `SAM2 Video Detector (SEGS)` – Similar to `Simple Detector for Video (SEGS)`, but utilizes SAM2’s video tracking technology to generate a SEGS object with a batch of masks.
* To use this node, you must select a SAM2 model in the SAMLoader.
### ControlNet, IPAdapter
* `ControlNetApply (SEGS)` - To apply ControlNet in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node.
* `segs_preprocessor` and `control_image` can be selectively applied. If 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.
### 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 nodes](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/detailers.md)
* `Detailer (SEGS)` - Refines the image based on SEGS.
* `Detailer (SEGS) with auto retry` - Refines the image based on SEGS and will automatically retry if the patch is all black.
* `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 Video` - Generates SEGS based on the mask for Video. (Renamed from `MASK to SEGS For AnimateDiff`)
* When using a single mask, convert it to SEGS to apply it to the entire frame.
* When using a batch mask, the contour fill feature is disabled.
* `MediaPipe FaceMesh to SEGS` - Separate each landmark from the mediapipe facemesh image to create labeled SEGS.
* 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.
* `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.
* 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.
* 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 Filter (non max suppression)` - This node filters SEGS by removing those with high overlap based on the Intersection over Union (IoU) threshold, keeping only the most confident detections.
* `SEGS Filter (intersection)` - This node filters segs1, keeping only the SEGS that do not significantly overlap with any SEGS in segs2, based on the Intersection over Area (IoA) threshold.
* `SEGS Assign (label)` - Assign labels sequentially to SEGS. This node is useful when used with `[LAB]` of FaceDetailer.
* `SEGSConcat` - Concatenate segs1 and segs2. If source shape of segs1 and segs2 are different from segs2 will be ignored.
* `SEGS Merge` - SEGS contains multiple SEGs. SEGS Merge integrates several SEGs into a single merged SEG. The label is changed to `merged` and the confidence becomes the minimum confidence. The applied controlnet and cropped_image are removed.
* `Picker (SEGS)` - Among the input SEGS, you can select a specific SEG through a dialog. If no SEG is selected, it outputs an empty SEGS. Increasing the batch_size of SEGSDetailer can be used for the purpose of selecting from the candidates.
* `Set Default Image For SEGS` - Set a default image for SEGS. SEGS with images set this way do not need to have a fallback image set. When override is set to false, the original image is preserved.
* `Remove Image from SEGS` - Remove the image set for the SEGS that has been configured by "Set Default Image for SEGS" or SEGSDetailer. When the image for the SEGS is removed, the Detailer node will operate based on the currently processed image instead of the SEGS.
* `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.
* `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.
* 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.
* 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.
* 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.
* 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.
* Since this is the hook applied when pasting onto the original image, it has no effect on nodes like `SEGSDetailer`.
* `VariationNoiseDetailerHookProvider` - Apply variation seed to the detailer. It can be applied in multiple stages through combine.
* `CustomSamplerDetailerHookProvider` - Apply a hook that allows you to use a custom sampler in the Detailer nodes. When using `DetailerHookCombine`, the sampler from the first hook is applied.
* `LamaRemoverDetailerHookProvider` – Applies Lama Remover to the upscaled image during the detailing stage. If `skip_sampling` is set to True, Lama Remover can be used alone without the detailing stage, allowing it to simply remove detected regions.
* Not applicable for **AnimateDiff** detailers. When using `DetailerHookCombine`, `skip_sampling` is only applied if it is set to `True` for all hooks.
* To use this node, the node pack at [Layer-norm/comfyui-lama-remover](https://github.com/Layer-norm/comfyui-lama-remover) must be installed.
### Iterative Upscale nodes
* `Iterative Upscale (Latent/on Pixel Space)` - The upscaler takes the input upscaler and splits the scale_factor into steps, then iteratively performs upscaling.
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)'.
### 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.
* 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.
* 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.
* 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.
* 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.
* 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.
* NOTE: See this [tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/switch.md)
### [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.
* 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.
* 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.
* 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.
### Impact KSampler
* These samplers support basic_pipe and AYS/OSS/GITS scheduler
* `KSampler (pipe)` - pipe version of KSampler
* `KSampler (advanced/pipe)` - pipe version of KSamplerAdvacned
* When converting the scheduler widget to input, refer to the `Impact Scheduler Adapter` node to resolve compatibility issues.
* `GITSScheduler Func Provider` - provider scheduler function for GITSScheduler
### 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.
* `Select Nth Item (Any list)` - Selects the Nth item from a list. If the index is out of range, it returns the last item in the list.
### Logics (experimental)
* These nodes are experimental nodes designed to implement the logic for loops and dynamic switching.
* `ImpactCompare`, `ImpactConditionalBranch`, `ImpactConditionalBranchSelMode`, `ImpactInt`, `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.
* 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)).
### Limitation
* Many nodes in the `Impact Pack` use a wildcard type to allow arbitrary input/output connections. This approach will be replaced once ComfyUI officially supports **dynamic types**. Until then, while it functions without issues, type validation may still produce error messages.
### 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.
* `SEGS Classify` - This node utilizes the `TRANSFORMERS_CLASSIFIER` loaded with 'HF Transformers Classifier Provider' to classify `SEGS`.
* The 'expr' allows for forms like `label > number`, and in the case of `preset_expr` being `Manual expr`, it uses the expression entered in `manual_expr`.
* For example, in the case of `male <= 0.4`, if the score of the `male` label in the classification result is less than or equal to 0.4, it is categorized as `filtered_SEGS`, otherwise, it is categorized as `remained_SEGS`.
* For supported labels, please refer to the `config.json` of the respective HuggingFace repository.
* `#Female` and `#Male` are symbols that group multiple labels such as `Female, women, woman, ...`, for convenience, rather than being single labels.
### Etc nodes
* `Impact Scheduler Adapter` - With the addition of AYS to the scheduler of the Impact Pack and Inspire Pack, there is an issue of incompatibility when the existing scheduler widget is converted to input. The Impact Scheduler Adapter allows for an indirect connection to be possible.
* `StringListToString` - Convert String List to String
* `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)'.
* 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.
## How To Install?
### Install via ComfyUI-Manager (Recommended)
* Search `ComfyUI Impact Pack` in ComfyUI-Manager and click `Install` button.
### Manual Install (Not Recommended)
1. `cd custom_nodes`
2. `git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack`
3. `cd ComfyUI-Impact-Pack`
4. `pip install -r requirements.txt`
* **IMPORTANT**:
* You must install it within the Python environment where ComfyUI is running.
* For the portable version, use `<installed path>\python_embeded\python.exe -m pip` instead of `pip`. For a `venv`, activate the `venv` first and then use `pip`.
5. Restart ComfyUI
* NOTE1: If an error occurs during the installation process, please refer to [Troubleshooting Page](troubleshooting/TROUBLESHOOTING.md) for assistance.
* NOTE2: You can use this colab notebook [colab notebook](https://colab.research.google.com/github/ltdrdata/ComfyUI-Impact-Pack/blob/Main/notebook/comfyui_colab_impact_pack.ipynb) to launch it. This notebook automatically downloads the impact pack to the custom_nodes directory, installs the tested dependencies, and runs it.
* NOTE3: If you create an empty file named `skip_download_model` in the `ComfyUI/custom_nodes/` directory, it will skip the model download step during the installation of the impact pack.
## Package Dependencies (If you need to manual setup.)
* pip install
* segment-anything
* scikit-image
* piexif
* opencv-python
* scipy
* numpy<2
* dill
* matplotlib
* (optional) onnxruntime
* (deprecated) openmim # for mim
* (deprecated) pycocotools # for mim
* linux packages (ubuntu)
* libgl1-mesa-glx
* libglib2.0-0
## Config example
* Once you run the Impact Pack for the first time, an `impact-pack.ini` file will be automatically generated in the Impact Pack directory. You can modify this configuration file to customize the default behavior.
* `dependency_version` - don't touch this
* `sam_editor_cpu` - use cpu for `SAM editor` instead of gpu
* sam_editor_model: Specify the SAM model for the SAM editor.
* You can download various SAM models using ComfyUI-Manager.
* Path to SAM model: `ComfyUI/models/sams`
```
[default]
sam_editor_cpu = False
sam_editor_model = sam_vit_b_01ec64.pth
```
## Other Materials (auto-download when installing)
* 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)
#### 1. Basic auto face detection and refine exapmle.
![simple](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple.png)
* The face that has been damaged due to low resolution is restored with high resolution by generating and synthesizing it, in order to restore the details.
* The FaceDetailer node is a combination of a Detector node for face detection and a Detailer node for image enhancement. See the [Advanced Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/tutorial/advanced.md) for a more detailed explanation.
* The MASK output of FaceDetailer provides a visualization of where the detected and enhanced areas are.
![simple-orig](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple-original.png) ![simple-refined](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple-refined.png)
* You can see that the face in the image on the left has increased detail as in the image on the right.
#### 2. 2Pass refine (restore a severely damaged face)
![2pass-workflow-example](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-simple.png)
* Although two FaceDetailers can be attached together for a 2-pass configuration, various common inputs used in KSampler can be passed through DETAILER_PIPE, so FaceDetailerPipe can be used to configure easily.
* In 1pass, only rough outline recovery is required, so restore with a reasonable resolution and low options. However, if you increase the dilation at this time, not only the face but also the surrounding parts are included in the recovery range, so it is useful when you need to reshape the face other than the facial part.
![2pass-example-original](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-original.png) ![2pass-example-middle](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-1pass.png) ![2pass-example-result](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-2pass.png)
* In the first stage, the severely damaged face is restored to some extent, and in the second stage, the details are restored
#### 3. Face Bbox(bounding box) + Person silhouette segmentation (prevent distortion of the background.)
![combination-workflow-example](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/combination.jpg)
![combination-example-original](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/combination-original.png) ![combination-example-refined](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/combination-refined.png)
* Facial synthesis that emphasizes details is delicately aligned with the contours of the face, and it can be observed that it does not affect the image outside of the face.
* The BBoxDetectorForEach node is used to detect faces, and the SAMDetectorCombined node is used to find the segment related to the detected face. By using the Segs & Mask node with the two masks obtained in this way, an accurate mask that intersects based on segs can be generated. If this generated mask is input to the DetailerForEach node, only the target area can be created in high resolution from the image and then composited.
#### 4. Iterative Upscale
![upscale-workflow-example](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/upscale-workflow.png)
* The IterativeUpscale node is a node that enlarges an image/latent by a scale_factor. In this process, the upscale is carried out progressively by dividing it into steps.
* IterativeUpscale takes an Upscaler as an input, similar to a plugin, and uses it during each iteration. PixelKSampleUpscalerProvider is an Upscaler that converts the latent representation to pixel space and applies ksampling.
* The upscale_model_opt is an optional parameter that determines whether to use the upscale function of the model base if available. Using the upscale function of the model base can significantly reduce the number of iterative steps required. If an x2 upscaler is used, the image/latent is first upscaled by a factor of 2 and then downscaled to the target scale at each step before further processing is done.
* The following image is an image of 304x512 pixels and the same image scaled up to three times its original size using IterativeUpscale.
![combination-example-original](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/upscale-original.png) ![combination-example-refined](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/upscale-3x.png)
#### 5. Interactive SAM Detector (Clipspace)
* When you right-click on the node that outputs 'MASK' and 'IMAGE', a menu called "Open in SAM Detector" appears, as shown in the following picture. Clicking on the menu opens a dialog in SAM's functionality, allowing you to generate a segment mask.
![samdetector-menu](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/SAMDetector-menu.png)
* By clicking the left mouse button on a coordinate, a positive prompt in blue color is entered, indicating the area that should be included. Clicking the right mouse button on a coordinate enters a negative prompt in red color, indicating the area that should be excluded. Positive prompts represent the areas that should be included, while negative prompts represent the areas that should be excluded.
* You can remove the points that were added by using the "undo" button. After selecting the points, pressing the "detect" button generates the mask. Additionally, you can adjust the fidelity slider to determine the extent to which the mask belongs to the confidence region.
![samdetector-dialog](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/SAMDetector-dialog.jpg)
* If you opened the dialog through "Open in SAM Detector" from the node, you can directly apply the changes by clicking the "Save to node" button. However, if you opened the dialog through the "clipspace" menu, you can save it to clipspace by clicking the "Save" button.
![samdetector-result](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/SAMDetector-result.jpg)
* When you execute using the reflected mask in the node, you can observe that the image and mask are displayed separately.
## Others Tutorials
* [ComfyUI-extension-tutorials/ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-extension-tutorials/tree/Main/ComfyUI-Impact-Pack) - You can find various tutorials and workflows on this page.
* [Advanced Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/advanced.md)
* [SAM Application](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/sam.md)
* [PreviewBridge](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/previewbridge.md)
* [Mask Pointer](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/maskpointer.md)
* [ONNX Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ONNX.md)
* [CLIPSeg Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/clipseg.md)
* [Extreme Highresolution Upscale](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/extreme-upscale.md)
* [TwoSamplersForMask](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoSamplers.md)
* [TwoAdvancedSamplersForMask](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoAdvancedSamplers.md)
* [Advanced Iterative Upscale: PK_HOOK](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/pk_hook.md)
* [Advanced Iterative Upscale: TwoSamplersForMask Upscale Provider](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoSamplersUpscale.md)
* [Interactive SAM + PreviewBridge](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/sam_with_preview_bridge.md)
* [ImageSender/ImageReceiver/LatentSender/LatentReceiver](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/sender_receiver.md)
* [ImpactWildcardProcessor](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcardProcessor.md)
## Credits
ComfyUI/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI.
dustysys/[ddetailer](https://github.com/dustysys/ddetailer) - DDetailer for Stable-diffusion-webUI extension.
Bing-su/[dddetailer](https://github.com/Bing-su/dddetailer) - The anime-face-detector used in ddetailer has been updated to be compatible with mmdet 3.0.0, and we have also applied a patch to the pycocotools dependency for Windows environment in ddetailer.
facebook/[segment-anything](https://github.com/facebookresearch/segment-anything) - Segmentation Anything!
hysts/[anime-face-detector](https://github.com/hysts/anime-face-detector) - Creator of `anime-face_yolov3`, which has impressive performance on a variety of art styles.
open-mmlab/[mmdetection](https://github.com/open-mmlab/mmdetection) - Object detection toolset. `dd-person_mask2former` was trained via transfer learning using their [R-50 Mask2Former instance segmentation model](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former#instance-segmentation) as a base.
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_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.
Trung0246/[ComfyUI-0246](https://github.com/Trung0246/ComfyUI-0246) - Nice bypass hack!
Layer-norm/[comfyui-lama-remover](https://github.com/Layer-norm/comfyui-lama-remover) - Required for using `LamaRemoverDetailerHook`.

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"""
@author: Dr.Lt.Data
@title: Impact Pack
@nickname: Impact Pack
@description: This extension offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler.
"""
import folder_paths
import os
import sys
import logging
comfy_path = os.path.dirname(folder_paths.__file__)
impact_path = os.path.join(os.path.dirname(__file__))
modules_path = os.path.join(os.path.dirname(__file__), "modules")
sys.path.append(modules_path)
import impact.config
logging.info(f"### Loading: ComfyUI-Impact-Pack ({impact.config.version})")
# Core
# recheck dependencies for colab
try:
import folder_paths
import torch # noqa: F401
import cv2 # noqa: F401
from cv2 import setNumThreads # noqa: F401
import numpy as np # noqa: F401
import comfy.samplers
import comfy.sd # noqa: F401
from PIL import Image, ImageFilter # noqa: F401
from skimage.measure import label, regionprops # noqa: F401
from collections import namedtuple # noqa: F401
import piexif # noqa: F401
import nodes
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
from .modules.impact.impact_pack import * # noqa: F403
from .modules.impact.detectors import * # noqa: F403
from .modules.impact.pipe import * # noqa: F403
from .modules.impact.logics import * # noqa: F403
from .modules.impact.util_nodes import * # noqa: F403
from .modules.impact.segs_nodes import * # noqa: F403
from .modules.impact.special_samplers import * # noqa: F403
from .modules.impact.hf_nodes import * # noqa: F403
from .modules.impact.bridge_nodes import * # noqa: F403
from .modules.impact.hook_nodes import * # noqa: F403
from .modules.impact.animatediff_nodes import * # noqa: F403
from .modules.impact.segs_upscaler import * # noqa: F403
import threading
threading.Thread(target=impact.wildcards.wildcard_load).start()
NODE_CLASS_MAPPINGS = {
"SAMLoader": SAMLoader, # noqa: F405
"CLIPSegDetectorProvider": CLIPSegDetectorProvider, # noqa: F405
"ONNXDetectorProvider": ONNXDetectorProvider, # noqa: F405
"BitwiseAndMaskForEach": BitwiseAndMaskForEach, # noqa: F405
"SubtractMaskForEach": SubtractMaskForEach, # noqa: F405
"DetailerForEach": DetailerForEach, # noqa: F405
"DetailerForEachAutoRetry": DetailerForEachAutoRetry, # noqa: F405
"DetailerForEachDebug": DetailerForEachTest, # noqa: F405
"DetailerForEachPipe": DetailerForEachPipe, # noqa: F405
"DetailerForEachDebugPipe": DetailerForEachTestPipe, # noqa: F405
"DetailerForEachPipeForAnimateDiff": DetailerForEachPipeForAnimateDiff, # noqa: F405
"SAMDetectorCombined": SAMDetectorCombined, # noqa: F405
"SAMDetectorSegmented": SAMDetectorSegmented, # noqa: F405
"FaceDetailer": FaceDetailer, # noqa: F405
"FaceDetailerPipe": FaceDetailerPipe, # noqa: F405
"MaskDetailerPipe": MaskDetailerPipe, # noqa: F405
"ToDetailerPipe": ToDetailerPipe, # noqa: F405
"ToDetailerPipeSDXL": ToDetailerPipeSDXL, # noqa: F405
"FromDetailerPipe": FromDetailerPipe, # noqa: F405
"FromDetailerPipe_v2": FromDetailerPipe_v2, # noqa: F405
"FromDetailerPipeSDXL": FromDetailerPipe_SDXL, # noqa: F405
"AnyPipeToBasic": AnyPipeToBasic, # noqa: F405
"ToBasicPipe": ToBasicPipe, # noqa: F405
"FromBasicPipe": FromBasicPipe, # noqa: F405
"FromBasicPipe_v2": FromBasicPipe_v2, # noqa: F405
"BasicPipeToDetailerPipe": BasicPipeToDetailerPipe, # noqa: F405
"BasicPipeToDetailerPipeSDXL": BasicPipeToDetailerPipeSDXL, # noqa: F405
"DetailerPipeToBasicPipe": DetailerPipeToBasicPipe, # noqa: F405
"EditBasicPipe": EditBasicPipe, # noqa: F405
"EditDetailerPipe": EditDetailerPipe, # noqa: F405
"EditDetailerPipeSDXL": EditDetailerPipeSDXL, # noqa: F405
"LatentPixelScale": LatentPixelScale, # noqa: F405
"PixelKSampleUpscalerProvider": PixelKSampleUpscalerProvider, # noqa: F405
"PixelKSampleUpscalerProviderPipe": PixelKSampleUpscalerProviderPipe, # noqa: F405
"IterativeLatentUpscale": IterativeLatentUpscale, # noqa: F405
"IterativeImageUpscale": IterativeImageUpscale, # noqa: F405
"PixelTiledKSampleUpscalerProvider": PixelTiledKSampleUpscalerProvider, # noqa: F405
"PixelTiledKSampleUpscalerProviderPipe": PixelTiledKSampleUpscalerProviderPipe, # noqa: F405
"TwoSamplersForMaskUpscalerProvider": TwoSamplersForMaskUpscalerProvider, # noqa: F405
"TwoSamplersForMaskUpscalerProviderPipe": TwoSamplersForMaskUpscalerProviderPipe, # noqa: F405
"PixelKSampleHookCombine": PixelKSampleHookCombine, # noqa: F405
"DenoiseScheduleHookProvider": DenoiseScheduleHookProvider, # noqa: F405
"StepsScheduleHookProvider": StepsScheduleHookProvider, # noqa: F405
"CfgScheduleHookProvider": CfgScheduleHookProvider, # noqa: F405
"NoiseInjectionHookProvider": NoiseInjectionHookProvider, # noqa: F405
"UnsamplerHookProvider": UnsamplerHookProvider, # noqa: F405
"CoreMLDetailerHookProvider": CoreMLDetailerHookProvider, # noqa: F405
"PreviewDetailerHookProvider": PreviewDetailerHookProvider, # noqa: F405
"BlackPatchRetryHookProvider": BlackPatchRetryHookProvider, # noqa: F405
"CustomSamplerDetailerHookProvider": CustomSamplerDetailerHookProvider, # noqa: F405
"LamaRemoverDetailerHookProvider": LamaRemoverDetailerHookProvider, # noqa: F405
"DetailerHookCombine": DetailerHookCombine, # noqa: F405
"NoiseInjectionDetailerHookProvider": NoiseInjectionDetailerHookProvider, # noqa: F405
"UnsamplerDetailerHookProvider": UnsamplerDetailerHookProvider, # noqa: F405
"DenoiseSchedulerDetailerHookProvider": DenoiseSchedulerDetailerHookProvider, # noqa: F405
"SEGSOrderedFilterDetailerHookProvider": SEGSOrderedFilterDetailerHookProvider, # noqa: F405
"SEGSRangeFilterDetailerHookProvider": SEGSRangeFilterDetailerHookProvider, # noqa: F405
"SEGSLabelFilterDetailerHookProvider": SEGSLabelFilterDetailerHookProvider, # noqa: F405
"VariationNoiseDetailerHookProvider": VariationNoiseDetailerHookProvider, # noqa: F405
# "CustomNoiseDetailerHookProvider": CustomNoiseDetailerHookProvider,
"BitwiseAndMask": BitwiseAndMask, # noqa: F405
"SubtractMask": SubtractMask, # noqa: F405
"AddMask": AddMask, # noqa: F405
"MaskRectArea": MaskRectArea, # noqa: F405
"MaskRectAreaAdvanced": MaskRectAreaAdvanced, # noqa: F405
"ImpactSegsAndMask": SegsBitwiseAndMask, # noqa: F405
"ImpactSegsAndMaskForEach": SegsBitwiseAndMaskForEach, # noqa: F405
"EmptySegs": EmptySEGS, # noqa: F405
"ImpactFlattenMask": FlattenMask, # noqa: F405
"MediaPipeFaceMeshToSEGS": MediaPipeFaceMeshToSEGS, # noqa: F405
"MaskToSEGS": MaskToSEGS, # noqa: F405
"MaskToSEGS_for_AnimateDiff": MaskToSEGS_for_AnimateDiff, # noqa: F405
"ToBinaryMask": ToBinaryMask, # noqa: F405
"MasksToMaskList": MasksToMaskList, # noqa: F405
"MaskListToMaskBatch": MaskListToMaskBatch, # noqa: F405
"ImageListToImageBatch": ImageListToImageBatch, # noqa: F405
"SetDefaultImageForSEGS": DefaultImageForSEGS, # noqa: F405
"RemoveImageFromSEGS": RemoveImageFromSEGS, # noqa: F405
"BboxDetectorSEGS": BboxDetectorForEach, # noqa: F405
"SegmDetectorSEGS": SegmDetectorForEach, # noqa: F405
"ONNXDetectorSEGS": BboxDetectorForEach, # noqa: F405
"ImpactSimpleDetectorSEGS_for_AD": SimpleDetectorForAnimateDiff, # noqa: F405
"ImpactSAM2VideoDetectorSEGS": SAM2VideoDetectorSEGS, # noqa: F405
"ImpactSimpleDetectorSEGS": SimpleDetectorForEach, # noqa: F405
"ImpactSimpleDetectorSEGSPipe": SimpleDetectorForEachPipe, # noqa: F405
"ImpactControlNetApplySEGS": ControlNetApplySEGS, # noqa: F405
"ImpactControlNetApplyAdvancedSEGS": ControlNetApplyAdvancedSEGS, # noqa: F405
"ImpactControlNetClearSEGS": ControlNetClearSEGS, # noqa: F405
"ImpactIPAdapterApplySEGS": IPAdapterApplySEGS, # noqa: F405
"ImpactDecomposeSEGS": DecomposeSEGS, # noqa: F405
"ImpactAssembleSEGS": AssembleSEGS, # noqa: F405
"ImpactFrom_SEG_ELT": From_SEG_ELT, # noqa: F405
"ImpactEdit_SEG_ELT": Edit_SEG_ELT, # noqa: F405
"ImpactDilate_Mask_SEG_ELT": Dilate_SEG_ELT, # noqa: F405
"ImpactDilateMask": DilateMask, # noqa: F405
"ImpactGaussianBlurMask": GaussianBlurMask, # noqa: F405
"ImpactDilateMaskInSEGS": DilateMaskInSEGS, # noqa: F405
"ImpactGaussianBlurMaskInSEGS": GaussianBlurMaskInSEGS, # noqa: F405
"ImpactScaleBy_BBOX_SEG_ELT": SEG_ELT_BBOX_ScaleBy, # noqa: F405
"ImpactFrom_SEG_ELT_bbox": From_SEG_ELT_bbox, # noqa: F405
"ImpactFrom_SEG_ELT_crop_region": From_SEG_ELT_crop_region, # noqa: F405
"ImpactCount_Elts_in_SEGS": Count_Elts_in_SEGS, # noqa: F405
"BboxDetectorCombined_v2": BboxDetectorCombined, # noqa: F405
"SegmDetectorCombined_v2": SegmDetectorCombined, # noqa: F405
"SegsToCombinedMask": SegsToCombinedMask, # noqa: F405
"KSamplerProvider": KSamplerProvider, # noqa: F405
"TwoSamplersForMask": TwoSamplersForMask, # noqa: F405
"TiledKSamplerProvider": TiledKSamplerProvider, # noqa: F405
"KSamplerAdvancedProvider": KSamplerAdvancedProvider, # noqa: F405
"TwoAdvancedSamplersForMask": TwoAdvancedSamplersForMask, # noqa: F405
"ImpactNegativeConditioningPlaceholder": NegativeConditioningPlaceholder, # noqa: F405
"PreviewBridge": PreviewBridge, # noqa: F405
"PreviewBridgeLatent": PreviewBridgeLatent, # noqa: F405
"ImageSender": ImageSender, # noqa: F405
"ImageReceiver": ImageReceiver, # noqa: F405
"LatentSender": LatentSender, # noqa: F405
"LatentReceiver": LatentReceiver, # noqa: F405
"ImageMaskSwitch": ImageMaskSwitch, # noqa: F405
"LatentSwitch": GeneralSwitch, # noqa: F405
"SEGSSwitch": GeneralSwitch, # noqa: F405
"ImpactSwitch": GeneralSwitch, # noqa: F405
"ImpactInversedSwitch": GeneralInversedSwitch, # noqa: F405
"ImpactWildcardProcessor": ImpactWildcardProcessor, # noqa: F405
"ImpactWildcardEncode": ImpactWildcardEncode, # noqa: F405
"SEGSUpscaler": SEGSUpscaler, # noqa: F405
"SEGSUpscalerPipe": SEGSUpscalerPipe, # noqa: F405
"SEGSDetailer": SEGSDetailer, # noqa: F405
"SEGSPaste": SEGSPaste, # noqa: F405
"SEGSPreview": SEGSPreview, # noqa: F405
"SEGSPreviewCNet": SEGSPreviewCNet, # noqa: F405
"SEGSToImageList": SEGSToImageList, # noqa: F405
"ImpactSEGSToMaskList": SEGSToMaskList, # noqa: F405
"ImpactSEGSToMaskBatch": SEGSToMaskBatch, # noqa: F405
"ImpactSEGSConcat": SEGSConcat, # noqa: F405
"ImpactSEGSPicker": SEGSPicker, # noqa: F405
"ImpactMakeTileSEGS": MakeTileSEGS, # noqa: F405
"ImpactSEGSMerge": SEGSMerge, # noqa: F405
"SEGSDetailerForAnimateDiff": SEGSDetailerForAnimateDiff, # noqa: F405
"ImpactKSamplerBasicPipe": KSamplerBasicPipe, # noqa: F405
"ImpactKSamplerAdvancedBasicPipe": KSamplerAdvancedBasicPipe, # noqa: F405
"ReencodeLatent": ReencodeLatent, # noqa: F405
"ReencodeLatentPipe": ReencodeLatentPipe, # noqa: F405
"ImpactImageBatchToImageList": ImageBatchToImageList, # noqa: F405
"ImpactMakeImageList": MakeImageList, # noqa: F405
"ImpactMakeImageBatch": MakeImageBatch, # noqa: F405
"ImpactMakeAnyList": MakeAnyList, # noqa: F405
"ImpactMakeMaskList": MakeMaskList, # noqa: F405
"ImpactMakeMaskBatch": MakeMaskBatch, # noqa: F405
"ImpactSelectNthItemOfAnyList": NthItemOfAnyList, # noqa: F405
"RegionalSampler": RegionalSampler, # noqa: F405
"RegionalSamplerAdvanced": RegionalSamplerAdvanced, # noqa: F405
"CombineRegionalPrompts": CombineRegionalPrompts, # noqa: F405
"RegionalPrompt": RegionalPrompt, # noqa: F405
"ImpactCombineConditionings": CombineConditionings, # noqa: F405
"ImpactConcatConditionings": ConcatConditionings, # noqa: F405
"ImpactSEGSLabelAssign": SEGSLabelAssign, # noqa: F405
"ImpactSEGSLabelFilter": SEGSLabelFilter, # noqa: F405
"ImpactSEGSRangeFilter": SEGSRangeFilter, # noqa: F405
"ImpactSEGSOrderedFilter": SEGSOrderedFilter, # noqa: F405
"ImpactSEGSIntersectionFilter": SEGSIntersectionFilter, # noqa: F405
"ImpactSEGSNMSFilter": SEGSNMSFilter, # noqa: F405
"ImpactCompare": ImpactCompare, # noqa: F405
"ImpactConditionalBranch": ImpactConditionalBranch, # noqa: F405
"ImpactConditionalBranchSelMode": ImpactConditionalBranchSelMode, # noqa: F405
"ImpactIfNone": ImpactIfNone, # noqa: F405
"ImpactConvertDataType": ImpactConvertDataType, # noqa: F405
"ImpactLogicalOperators": ImpactLogicalOperators, # noqa: F405
"ImpactInt": ImpactInt, # noqa: F405
"ImpactFloat": ImpactFloat, # noqa: F405
"ImpactBoolean": ImpactBoolean, # noqa: F405
"ImpactValueSender": ImpactValueSender, # noqa: F405
"ImpactValueReceiver": ImpactValueReceiver, # noqa: F405
"ImpactImageInfo": ImpactImageInfo, # noqa: F405
"ImpactLatentInfo": ImpactLatentInfo, # noqa: F405
"ImpactMinMax": ImpactMinMax, # noqa: F405
"ImpactNeg": ImpactNeg, # noqa: F405
"ImpactConditionalStopIteration": ImpactConditionalStopIteration, # noqa: F405
"ImpactStringSelector": StringSelector, # noqa: F405
"StringListToString": StringListToString, # noqa: F405
"WildcardPromptFromString": WildcardPromptFromString, # noqa: F405
"ImpactExecutionOrderController": ImpactExecutionOrderController, # noqa: F405
"ImpactListBridge": ImpactListBridge, # noqa: F405
"RemoveNoiseMask": RemoveNoiseMask, # noqa: F405
"ImpactLogger": ImpactLogger, # noqa: F405
"ImpactDummyInput": ImpactDummyInput, # noqa: F405
"ImpactQueueTrigger": ImpactQueueTrigger, # noqa: F405
"ImpactQueueTriggerCountdown": ImpactQueueTriggerCountdown, # noqa: F405
"ImpactSetWidgetValue": ImpactSetWidgetValue, # noqa: F405
"ImpactNodeSetMuteState": ImpactNodeSetMuteState, # noqa: F405
"ImpactControlBridge": ImpactControlBridge, # noqa: F405
"ImpactIsNotEmptySEGS": ImpactNotEmptySEGS, # noqa: F405
"ImpactSleep": ImpactSleep, # noqa: F405
"ImpactRemoteBoolean": ImpactRemoteBoolean, # noqa: F405
"ImpactRemoteInt": ImpactRemoteInt, # noqa: F405
"ImpactHFTransformersClassifierProvider": HF_TransformersClassifierProvider, # noqa: F405
"ImpactSEGSClassify": SEGS_Classify, # noqa: F405
"ImpactSchedulerAdapter": ImpactSchedulerAdapter, # noqa: F405
"GITSSchedulerFuncProvider": GITSSchedulerFuncProvider # noqa: F405
}
NODE_DISPLAY_NAME_MAPPINGS = {
"SAMLoader": "SAMLoader (Impact)",
"BboxDetectorSEGS": "BBOX Detector (SEGS)",
"SegmDetectorSEGS": "SEGM Detector (SEGS)",
"ONNXDetectorSEGS": "ONNX Detector (SEGS/legacy) - use BBOXDetector",
"ImpactSimpleDetectorSEGS_for_AD": "Simple Detector for Video (SEGS)",
"ImpactSAM2VideoDetectorSEGS": "SAM2 Video Detector (SEGS)",
"ImpactSimpleDetectorSEGS": "Simple Detector (SEGS)",
"ImpactSimpleDetectorSEGSPipe": "Simple Detector (SEGS/pipe)",
"ImpactControlNetApplySEGS": "ControlNetApply (SEGS) - DEPRECATED",
"ImpactControlNetApplyAdvancedSEGS": "ControlNetApply (SEGS)",
"ImpactIPAdapterApplySEGS": "IPAdapterApply (SEGS)",
"BboxDetectorCombined_v2": "BBOX Detector (combined)",
"SegmDetectorCombined_v2": "SEGM Detector (combined)",
"SegsToCombinedMask": "SEGS to MASK (combined)",
"MediaPipeFaceMeshToSEGS": "MediaPipe FaceMesh to SEGS",
"MaskToSEGS": "MASK to SEGS",
"MaskToSEGS_for_AnimateDiff": "MASK to SEGS for Video",
"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)",
"DetailerForEachAutoRetry": "Detailer (SEGS) with auto retry",
"DetailerForEachPipe": "Detailer (SEGS/pipe)",
"DetailerForEachDebug": "DetailerDebug (SEGS)",
"DetailerForEachDebugPipe": "DetailerDebug (SEGS/pipe)",
"SEGSDetailerForAnimateDiff": "SEGSDetailer For Video (SEGS/pipe)",
"DetailerForEachPipeForAnimateDiff": "Detailer For Video (SEGS/pipe)",
"SEGSUpscaler": "Upscaler (SEGS)",
"SEGSUpscalerPipe": "Upscaler (SEGS/pipe)",
"SAMDetectorCombined": "SAMDetector (combined)",
"SAMDetectorSegmented": "SAMDetector (segmented)",
"FaceDetailerPipe": "FaceDetailer (pipe)",
"MaskDetailerPipe": "MaskDetailer (pipe)",
"FromDetailerPipeSDXL": "FromDetailer (SDXL/pipe)",
"BasicPipeToDetailerPipeSDXL": "BasicPipe -> DetailerPipe (SDXL)",
"EditDetailerPipeSDXL": "Edit DetailerPipe (SDXL)",
"BasicPipeToDetailerPipe": "BasicPipe -> DetailerPipe",
"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)",
"IterativeImageUpscale": "Iterative Upscale (Image)",
"TwoSamplersForMaskUpscalerProvider": "TwoSamplersForMask Upscaler Provider",
"TwoSamplersForMaskUpscalerProviderPipe": "TwoSamplersForMask Upscaler Provider (pipe)",
"ReencodeLatent": "Reencode Latent",
"ReencodeLatentPipe": "Reencode Latent (pipe)",
"ImpactKSamplerBasicPipe": "KSampler (pipe)",
"ImpactKSamplerAdvancedBasicPipe": "KSampler (Advanced/pipe)",
"ImpactSEGSLabelAssign": "SEGS Assign (label)",
"ImpactSEGSLabelFilter": "SEGS Filter (label)",
"ImpactSEGSRangeFilter": "SEGS Filter (range)",
"ImpactSEGSOrderedFilter": "SEGS Filter (ordered)",
"ImpactSEGSIntersectionFilter": "SEGS Filter (intersection)",
"ImpactSEGSNMSFilter": "SEGS Filter (non max suppression)",
"ImpactSEGSConcat": "SEGS Concat",
"ImpactSEGSToMaskList": "SEGS to Mask List",
"ImpactSEGSToMaskBatch": "SEGS to Mask Batch",
"ImpactSEGSPicker": "Picker (SEGS)",
"ImpactMakeTileSEGS": "Make Tile SEGS",
"ImpactSEGSMerge": "SEGS Merge",
"ImpactDecomposeSEGS": "Decompose (SEGS)",
"ImpactAssembleSEGS": "Assemble (SEGS)",
"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)",
"ImpactGaussianBlurMaskInSEGS": "Gaussian Blur Mask (SEGS)",
"PreviewBridge": "Preview Bridge (Image)",
"PreviewBridgeLatent": "Preview Bridge (Latent)",
"ImageSender": "Image Sender",
"ImageReceiver": "Image Receiver",
"ImageMaskSwitch": "Switch (images, mask)",
"ImpactSwitch": "Switch (Any)",
"ImpactInversedSwitch": "Inversed Switch (Any)",
"ImpactExecutionOrderController": "Execution Order Controller",
"ImpactListBridge": "List Bridge",
"MasksToMaskList": "Mask Batch to Mask List",
"MaskListToMaskBatch": "Mask List to Mask Batch",
"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)",
"ImpactSelectNthItemOfAnyList": "Select Nth Item (Any list)",
"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",
"RemoveNoiseMask": "Remove Noise Mask",
"ImpactCombineConditionings": "Combine Conditionings",
"ImpactConcatConditionings": "Concat Conditionings",
"ImpactQueueTrigger": "Queue Trigger",
"ImpactQueueTriggerCountdown": "Queue Trigger (Countdown)",
"ImpactSetWidgetValue": "Set Widget Value",
"ImpactNodeSetMuteState": "Set Mute State",
"ImpactControlBridge": "Control Bridge",
"ImpactSleep": "Sleep",
"ImpactRemoteBoolean": "Remote Boolean (on prompt)",
"ImpactRemoteInt": "Remote Int (on prompt)",
"ImpactHFTransformersClassifierProvider": "HF Transformers Classifier Provider",
"ImpactSEGSClassify": "SEGS Classify",
"LatentSwitch": "Switch (latent/legacy)",
"SEGSSwitch": "Switch (SEGS/legacy)",
"SEGSPreviewCNet": "SEGSPreview (CNET Image)",
"ImpactSchedulerAdapter": "Impact Scheduler Adapter",
"GITSSchedulerFuncProvider": "GITSScheduler Func Provider",
"ImpactNegativeConditioningPlaceholder": "Negative Cond Placeholder"
}
# 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')
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

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# Wildcard System Documentation
Progressive on-demand wildcard loading system for ComfyUI Impact Pack.
## Documentation Structure
- **[WILDCARD_SYSTEM_PRD.md](WILDCARD_SYSTEM_PRD.md)** - Product requirements and specifications
- **[WILDCARD_SYSTEM_DESIGN.md](WILDCARD_SYSTEM_DESIGN.md)** - Technical architecture and implementation
- **[WILDCARD_TESTING_GUIDE.md](WILDCARD_TESTING_GUIDE.md)** - Testing procedures and validation
## Quick Links
- Test Suite: `../../tests/`
- Test Samples: `../../tests/wildcards/samples/`
- Implementation: `../../modules/impact/wildcards.py`
- Server API: `../../modules/impact/impact_server.py`
## Test Execution
```bash
cd tests/
# Run all test suites
bash test_encoding.sh # UTF-8 multi-language (15 tests)
bash test_error_handling.sh # Error handling (10 tests)
bash test_edge_cases.sh # Edge cases (20 tests)
bash test_deep_nesting.sh # 7-level nesting (15 tests)
bash test_ondemand_loading.sh # On-demand loading (8 tests)
bash test_config_quotes.sh # Config quotes (5 tests)
```
## Status
✅ **Production Ready**
- 73 tests, 100% pass rate (6 test suites)
- Complete PRD coverage
- Zero implementation bugs
- UTF-8 encoding verified
- Error handling validated

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# Wildcard System - Project Summary
## Overview
Progressive on-demand wildcard loading system for ComfyUI Impact Pack with dynamic prompt support, UTF-8 encoding, and comprehensive testing.
**Status**: ✅ Production Ready
**Test Coverage**: 86 tests, 100% pass rate
**Documentation**: Complete PRD, design docs, and testing guide
---
## Core Features
- **Wildcard Expansion**: `__wildcard__` syntax with transitive multi-level expansion
- **Dynamic Prompts**:
- Basic selection: `{option1|option2|option3}`
- Weighted selection: `{10::common|1::rare}` (weight comes first)
- Multi-select: `{2$$, $$red|blue|green}` with custom separators
- **UTF-8 Support**: Korean, Chinese, Arabic, emoji, special characters
- **Pattern Matching**: Depth-agnostic `__*/name__` syntax
- **On-Demand Loading**: Progressive lazy loading with configurable cache limits
- **Error Handling**: Circular reference detection, graceful fallbacks
---
## Architecture
### Implementation
- `modules/impact/wildcards.py` - Core LazyWildcardLoader and expansion engine
- `modules/impact/impact_server.py` - Server API endpoint (/impact/wildcards)
- `modules/impact/config.py` - Configuration with quoted path support
### Key Design Decisions
- **Lazy Loading**: Memory-efficient progressive loading strategy
- **Transitive Expansion**: Multi-level wildcard references through directory hierarchy
- **Case-Insensitive Matching**: Fuzzy matching for user convenience
- **Circular Reference Detection**: Max 100 iterations with clear error messages
---
## Testing
### Test Suites (86 tests)
1. **UTF-8 Encoding** (15 tests) - Multi-language support validation
2. **Error Handling** (10 tests) - Graceful error recovery
3. **Edge Cases** (20 tests) - Boundary conditions and special scenarios
4. **Deep Nesting** (17 tests) - 7-level transitive expansion + pattern matching
5. **On-Demand Loading** (8 tests) - Progressive loading with cache limits
6. **Config Quotes** (5 tests) - Configuration path handling
7. **Dynamic Prompts** (11 tests) - Statistical validation of dynamic features
### Test Infrastructure
- Dedicated ports per suite (8188-8198)
- Automated server lifecycle management
- Comprehensive logging in `/tmp/`
- 100% pass rate with statistical validation
---
## Documentation
- **[README](README.md)** - Quick start and feature overview
- **[PRD](WILDCARD_SYSTEM_PRD.md)** - Complete product requirements
- **[Design](WILDCARD_SYSTEM_DESIGN.md)** - Technical architecture
- **[Testing Guide](WILDCARD_TESTING_GUIDE.md)** - Test procedures and validation
---
## Quick Start
### Basic Usage
```python
# Simple wildcard
"a photo of __animal__"
# Dynamic prompt
"a {red|blue|green} __vehicle__"
# Weighted selection (weight comes FIRST)
"{10::common|1::rare} scene"
# Multi-select
"{2$$, $$happy|sad|angry|excited} person"
```
### Running Tests
```bash
cd tests/
bash test_encoding.sh
bash test_error_handling.sh
bash test_edge_cases.sh
bash test_deep_nesting.sh
bash test_ondemand_loading.sh
bash test_config_quotes.sh
bash test_dynamic_prompts_full.sh
```
---
## Key Implementations
### Weighted Selection Syntax
**Correct**: `{weight::option}` - Weight comes FIRST
- `{10::common|1::rare}` → 91% common, 9% rare ✅
- `{5::red|3::green|2::blue}` → 50%, 30%, 20% ✅
**Incorrect**: `{option::weight}` - Treated as equal weights
- `{common::10|rare::1}` → 50% each ❌
### Empty Line Filtering
Filter empty lines AND comment lines:
```python
[x for x in lines if x.strip() and not x.strip().startswith('#')]
```
### Config Path Quotes
Strip quotes from configuration paths:
```python
custom_wildcards_path = default_conf.get('custom_wildcards', '').strip('\'"')
```
---
## Limitations
- Weighted selection supports integers and simple decimals only
- Complex decimal weights may conflict with multiselect pattern detection
- Circular references limited to 100 iterations
- Prefer integer weight ratios for clarity
---
## Performance
- **Lazy Loading**: Only load wildcards when needed
- **On-Demand Mode**: Progressive loading based on cache limits
- **Memory Efficient**: Configurable cache size (0.5MB - 100MB)
- **Fast Lookup**: Optimized directory traversal with pattern matching
---
## Production Ready
✅ Zero known bugs
✅ Complete PRD coverage
✅ 100% test pass rate
✅ Statistical validation
✅ Comprehensive documentation
✅ Multi-language support
✅ Graceful error handling

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# Wildcard System - Design Document
**Document Type**: Technical Design Document
**Product**: ComfyUI Impact Pack Wildcard System
**Version**: 2.0 (Depth-Agnostic Matching)
**Last Updated**: 2025-11-18
**Status**: Released
---
## 1. System Architecture
### 1.1 High-Level Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ ComfyUI Frontend │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ ImpactWildcardProcessor / ImpactWildcardEncode │ │
│ │ - Wildcard Prompt (editable) │ │
│ │ - Populated Prompt (read-only in Populate mode) │ │
│ │ - Mode: Populate / Fixed │ │
│ │ - UI Indicator: 🟢 Full Cache / 🔵 On-Demand │ │
│ └──────────────────────────────────────────────────────┘ │
└────────────────────────┬─────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Impact Server (API) │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ POST /impact/wildcards │ │
│ │ GET /impact/wildcards/list │ │
│ │ GET /impact/wildcards/list/loaded │ │
│ │ GET /impact/wildcards/refresh │ │
│ └──────────────────────────────────────────────────────┘ │
└────────────────────────┬─────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Wildcard Processing Engine │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ process() - Main entry point │ │
│ │ ├─ process_comment_out() │ │
│ │ ├─ replace_options() - {a|b|c} │ │
│ │ └─ replace_wildcard() - __wildcard__ │ │
│ │ │ │
│ │ get_wildcard_value() │ │
│ │ ├─ Direct lookup │ │
│ │ ├─ Depth-agnostic fallback ⭐ NEW │ │
│ │ └─ On-demand file loading │ │
│ │ │ │
│ │ get_wildcard_options() - {option1|__wild__|option3} │ │
│ │ └─ Pattern matching for wildcards in options │ │
│ └──────────────────────────────────────────────────────┘ │
└────────────────────────┬─────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Loading System │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ Startup Phase │ │
│ │ ├─ calculate_directory_size() - Early termination │ │
│ │ ├─ Determine mode (Full Cache / On-Demand) │ │
│ │ └─ scan_wildcard_metadata() - TXT metadata only │ │
│ │ │ │
│ │ Full Cache Mode │ │
│ │ └─ load_wildcards() - Load all data │ │
│ │ │ │
│ │ On-Demand Mode ⭐ NEW │ │
│ │ ├─ Pre-load: YAML files (keys in content) │ │
│ │ └─ On-demand: TXT files (path = key) │ │
│ └──────────────────────────────────────────────────────┘ │
└────────────────────────┬─────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Data Storage │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ wildcard_dict = {} │ │
│ │ - Full cache: All wildcard data │ │
│ │ - On-demand: Not used │ │
│ │ │ │
│ │ available_wildcards = {} ⭐ NEW │ │
│ │ - On-demand only: Metadata (path → file) │ │
│ │ - Example: {"dragon": "/path/dragon.txt"} │ │
│ │ │ │
│ │ loaded_wildcards = {} ⭐ NEW │ │
│ │ - On-demand only: Loaded data cache │ │
│ │ - Example: {"dragon": ["red dragon", "blue..."]} │ │
│ └──────────────────────────────────────────────────────┘ │
└────────────────────────┬─────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ File System │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ wildcards/ (bundled) │ │
│ │ custom_wildcards/ (user-defined) │ │
│ │ ├─ *.txt files (one option per line) │ │
│ │ └─ *.yaml files (nested structure) │ │
│ └──────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
```
---
## 2. Core Components
### 2.1 Processing Engine
#### 2.1.1 process()
**Purpose**: Main entry point for wildcard text processing
**Flow**:
```python
def process(text, seed=None):
1. process_comment_out(text) # Remove # comments
2. random.seed(seed) # Deterministic generation
3. replace_options(text) # Process {a|b|c}
4. replace_wildcard(text) # Process __wildcard__
5. return processed_text
```
**Features**:
- Maximum 100 iterations for nested expansion
- Deterministic with seed
- Supports transitive wildcards
---
#### 2.1.2 replace_options()
**Purpose**: Process dynamic prompts `{option1|option2}`
**Supported Syntax**:
```python
{a|b|c} # Random selection
{3::a|2::b|c} # Weighted (3:2:1 ratio)
{2$$, $$a|b|c|d} # Multi-select 2, comma-separated
{2-4$$; $$a|b|c|d} # Multi-select 2-4, semicolon-separated
{a|{b|c}|d} # Nested options
```
**Algorithm**:
1. Parse weight prefix (`::`)
2. Calculate normalized probabilities
3. Use `np.random.choice()` with probabilities
4. Handle multi-select with custom separators
---
#### 2.1.3 replace_wildcard()
**Purpose**: Process wildcard references `__wildcard__`
**Flow**:
```python
def replace_wildcard(string):
for each __match__:
1. keyword = normalize(match)
2. options = get_wildcard_value(keyword)
3. if options:
random select from options
elif '*' in keyword:
pattern matching (for __*/name__)
else:
keep unchanged
4. replace in string
```
**Pattern Matching** (`__*/name__`):
```python
if keyword.startswith('*/'):
base_name = keyword[2:] # "*/dragon" → "dragon"
for k in wildcards:
if matches_pattern(k, base_name):
collect options
combine all options
```
---
### 2.2 Depth-Agnostic Matching ⭐ NEW
#### 2.2.1 get_wildcard_value()
**Purpose**: Retrieve wildcard data with automatic depth-agnostic fallback
**Algorithm**:
```python
def get_wildcard_value(key):
# Phase 1: Direct lookup
if key in loaded_wildcards:
return loaded_wildcards[key]
# Phase 2: File discovery
file_path = find_wildcard_file(key)
if file_path:
load and cache
return data
# Phase 3: Depth-agnostic fallback ⭐ NEW
matched_keys = []
for k in available_wildcards:
if matches_depth_agnostic(k, key):
matched_keys.append(k)
if matched_keys:
# Combine all matched wildcards
all_options = []
for mk in matched_keys:
all_options.extend(get_wildcard_value(mk))
# Cache combined result
loaded_wildcards[key] = all_options
return all_options
return None
```
**Pattern Matching Logic**:
```python
def matches_depth_agnostic(stored_key, search_key):
"""
Examples:
search_key = "dragon"
stored_key = "dragon" → True (exact)
stored_key = "custom_wildcards/dragon" → True (ends with)
stored_key = "dragon/wizard" → True (starts with)
stored_key = "a/b/dragon/c/d" → True (contains)
"""
return (stored_key == search_key or
stored_key.endswith('/' + search_key) or
stored_key.startswith(search_key + '/') or
('/' + search_key + '/') in stored_key)
```
**Benefits**:
- Works with any directory structure
- No configuration needed
- Combines multiple sources for variety
- Cached for performance
---
### 2.3 Loading System
#### 2.3.1 Mode Detection
**Decision Algorithm**:
```python
def determine_loading_mode():
total_size = calculate_directory_size()
cache_limit = config.wildcard_cache_limit_mb * 1024 * 1024
if total_size >= cache_limit:
return ON_DEMAND_MODE
else:
return FULL_CACHE_MODE
```
**Early Termination**:
```python
def calculate_directory_size():
size = 0
for file in walk(directory):
size += file_size
if size >= cache_limit:
return size # Early termination
return size
```
**Performance**: < 1 second for 10GB+ collections
---
#### 2.3.2 Metadata Scanning ⭐ NEW
**Purpose**: Discover TXT wildcards without loading data
**Algorithm**:
```python
def scan_wildcard_metadata(path):
for file in walk(path):
if file.endswith('.txt'):
rel_path = relpath(file, path)
key = normalize(remove_extension(rel_path))
available_wildcards[key] = file # Store path only
```
**Storage**:
```python
available_wildcards = {
"dragon": "/path/custom_wildcards/dragon.txt",
"custom_wildcards/dragon": "/path/custom_wildcards/dragon.txt",
"dragon/wizard": "/path/dragon/wizard.txt",
...
}
```
**Memory**: ~50 bytes per file (path string)
---
#### 2.3.3 On-Demand Loading ⭐ NEW
**Purpose**: Load wildcard data only when accessed
**Flow**:
```
User request: __dragon__
↓
get_wildcard_value("dragon")
↓
Not in cache → find_wildcard_file("dragon")
↓
File not found → Depth-agnostic fallback
↓
Pattern match: ["custom_wildcards/dragon", "dragon/wizard", ...]
↓
Load each matched file
↓
Combine all options
↓
Cache result: loaded_wildcards["dragon"] = combined_options
↓
Return combined_options
```
**YAML Pre-Loading**:
```python
def load_yaml_wildcards():
"""
YAML wildcards CANNOT be on-demand because:
- Keys are inside file content, not file path
- Must parse entire file to discover keys
Example:
File: colors.yaml
Content:
warm: [red, orange, yellow]
cold: [blue, green, purple]
To know "__colors/warm__" exists, must parse entire file.
"""
for yaml_file in find_yaml_files():
data = yaml.load(yaml_file)
for key, value in data.items():
loaded_wildcards[key] = value
```
---
### 2.4 Data Structures
#### 2.4.1 Global State
```python
# Configuration
_on_demand_mode = False # True if on-demand mode active
wildcard_dict = {} # Full cache mode storage
available_wildcards = {} # On-demand metadata (key → file path)
loaded_wildcards = {} # On-demand loaded data (key → options)
# Thread safety
wildcard_lock = threading.Lock()
```
#### 2.4.2 Key Normalization
```python
def wildcard_normalize(x):
"""
Normalize wildcard keys for consistent lookup
Examples:
"Dragon" → "dragon" (lowercase)
"dragon.txt" → "dragon" (remove extension)
"folder/Dragon" → "folder/dragon" (lowercase)
"""
return x.lower().replace('\\', '/')
```
---
## 3. API Design
### 3.1 POST /impact/wildcards
**Purpose**: Process wildcard text
**Request**:
```json
{
"text": "a {red|blue} __flowers__",
"seed": 42
}
```
**Response**:
```json
{
"text": "a red rose"
}
```
**Implementation**:
```python
@app.post("/impact/wildcards")
def process_wildcards(request):
text = request.json["text"]
seed = request.json.get("seed")
result = process(text, seed)
return {"text": result}
```
---
### 3.2 GET /impact/wildcards/list/loaded ⭐ NEW
**Purpose**: Track progressive loading
**Response**:
```json
{
"data": ["__dragon__", "__flowers__"],
"on_demand_mode": true,
"total_available": 1000
}
```
**Implementation**:
```python
@app.get("/impact/wildcards/list/loaded")
def get_loaded_wildcards():
with wildcard_lock:
if _on_demand_mode:
return {
"data": [f"__{k}__" for k in loaded_wildcards.keys()],
"on_demand_mode": True,
"total_available": len(available_wildcards)
}
else:
return {
"data": [f"__{k}__" for k in wildcard_dict.keys()],
"on_demand_mode": False,
"total_available": len(wildcard_dict)
}
```
---
### 3.3 GET /impact/wildcards/refresh
**Purpose**: Reload all wildcards
**Implementation**:
```python
@app.get("/impact/wildcards/refresh")
def refresh_wildcards():
global wildcard_dict, loaded_wildcards, available_wildcards
with wildcard_lock:
# Clear all caches
wildcard_dict.clear()
loaded_wildcards.clear()
available_wildcards.clear()
# Re-initialize
wildcard_load()
return {"status": "ok"}
```
---
## 4. File Format Support
### 4.1 TXT Format
**Structure**:
```
# flowers.txt
rose
tulip
# Comments start with #
sunflower
```
**Parsing**:
```python
def load_txt_wildcard(file_path):
with open(file_path) as f:
lines = f.read().splitlines()
return [x for x in lines if not x.strip().startswith('#')]
```
**On-Demand**: ✅ Fully supported
---
### 4.2 YAML Format
**Structure**:
```yaml
# colors.yaml
warm:
- red
- orange
- yellow
cold:
- blue
- green
- purple
```
**Usage**: `__colors/warm__`, `__colors/cold__`
**Parsing**:
```python
def load_yaml_wildcard(file_path):
data = yaml.load(file_path)
for key, value in data.items():
if isinstance(value, list):
loaded_wildcards[key] = value
elif isinstance(value, dict):
# Recursive for nested structure
load_nested(key, value)
```
**On-Demand**: ⚠️ Always pre-loaded (keys in content)
---
## 5. UI Integration
### 5.1 ImpactWildcardProcessor Node
**Features**:
- **Wildcard Prompt**: User input with wildcard syntax
- **Populated Prompt**: Processed result
- **Mode Selector**: Populate / Fixed
- **Populate**: Process wildcards on queue, populate result
- **Fixed**: Use populated text as-is (for saved images)
**UI Indicator**:
- 🟢 **Full Cache**: All wildcards loaded
- 🔵 **On-Demand**: Progressive loading active (shows count)
---
### 5.2 ImpactWildcardEncode Node
**Additional Features**:
- **LoRA Loading**: `<lora:name:model_weight:clip_weight>`
- **LoRA Block Weight**: `<lora:name:1.0:1.0:LBW=spec;>`
- **BREAK Syntax**: Separate encoding with Concat
- **Clip Integration**: Returns processed model + clip
**Special Syntax**:
```
<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.;>
```
---
### 5.3 Detailer Wildcard Features
**Ordering**:
- `[ASC]`: Ascending order (x, y)
- `[DSC]`: Descending order (x, y)
- `[ASC-SIZE]`: Ascending by area
- `[DSC-SIZE]`: Descending by area
- `[RND]`: Random order
**Control**:
- `[SEP]`: Separate prompts per detection area
- `[SKIP]`: Skip detailing for this area
- `[STOP]`: Stop detailing (including current area)
- `[LAB]`: Label-based application
- `[CONCAT]`: Concatenate with positive conditioning
**Example**:
```
[ASC]
1girl, blue eyes, smile [SEP]
1boy, brown eyes [SEP]
```
---
## 6. Performance Optimization
### 6.1 Startup Optimization
**Techniques**:
1. **Early Termination**: Stop size calculation at cache limit
2. **Metadata Only**: Don't load TXT file content
3. **YAML Pre-loading**: Small files, pre-load is acceptable
**Results**:
- 10GB collection: 20-60 min → < 1 min (95%+ improvement)
---
### 6.2 Runtime Optimization
**Techniques**:
1. **Caching**: Store loaded wildcards in memory
2. **Depth-Agnostic Caching**: Cache combined pattern results
3. **NumPy Random**: Fast random generation
**Results**:
- First access: < 50ms
- Cached access: < 1ms
---
### 6.3 Memory Optimization
**Techniques**:
1. **Progressive Loading**: Load only accessed wildcards
2. **Metadata Storage**: Store paths, not data
3. **Combined Caching**: Cache pattern match results
**Results**:
- Initial: < 100MB (vs 1GB+ in old implementation)
- Growth: Linear with usage, not total size
---
## 7. Error Handling
### 7.1 File Not Found
**Scenario**: Wildcard file doesn't exist
**Handling**:
```python
def get_wildcard_value(key):
file_path = find_wildcard_file(key)
if file_path is None:
# Try depth-agnostic fallback
matched = find_pattern_matches(key)
if matched:
return combine_matched(matched)
# No match found - log warning, return None
logging.warning(f"Wildcard not found: {key}")
return None
```
**User Impact**: Wildcard remains unexpanded
---
### 7.2 File Read Error
**Scenario**: Cannot read file (permissions, encoding, etc.)
**Handling**:
```python
def load_txt_wildcard(file_path):
try:
with open(file_path, 'r', encoding="ISO-8859-1") as f:
return f.read().splitlines()
except Exception as e:
logging.error(f"Failed to load {file_path}: {e}")
return None
```
**User Impact**: Wildcard not loaded, error logged
---
### 7.3 Infinite Loop Protection
**Scenario**: Circular wildcard references
**Protection**:
```python
def process(text, seed=None):
max_iterations = 100
for i in range(max_iterations):
new_text = process_one_pass(text)
if new_text == text:
break # No changes, done
text = new_text
if i == max_iterations - 1:
logging.warning("Max iterations reached")
return text
```
**User Impact**: Processing stops after 100 iterations
---
## 8. Testing Strategy
### 8.1 Unit Tests
**Coverage**:
- `process()`: All syntax variations
- `replace_options()`: Weight, multi-select, nested
- `replace_wildcard()`: Direct, pattern, depth-agnostic
- `get_wildcard_value()`: Direct, fallback, caching
---
### 8.2 Integration Tests
**Scenarios**:
- Full cache mode activation
- On-demand mode activation
- Progressive loading tracking
- Depth-agnostic matching
- API endpoints
**Test Suite**: `tests/test_dragon_wildcard_expansion.sh`
---
### 8.3 Performance Tests
**Metrics**:
- Startup time (10GB collection)
- Memory usage (initial, after 100 accesses)
- First access latency
- Cached access latency
- Pattern matching latency
**Test Tool**: `/tmp/test_depth_agnostic.sh`
---
## 9. Security Considerations
### 9.1 Path Traversal
**Risk**: Malicious wildcard names could access files outside wildcard directory
**Mitigation**:
```python
def find_wildcard_file(key):
# Normalize and validate path
safe_key = os.path.normpath(key)
if '..' in safe_key or safe_key.startswith('/'):
logging.error(f"Invalid wildcard path: {key}")
return None
# Ensure result is within wildcard directory
file_path = os.path.join(wildcards_path, safe_key)
if not file_path.startswith(wildcards_path):
logging.error(f"Path traversal attempt: {key}")
return None
return file_path
```
---
### 9.2 Resource Exhaustion
**Risk**: Very large wildcards or infinite loops
**Mitigation**:
1. **Iteration Limit**: Max 100 expansions
2. **File Size Limit**: Reasonable file size checks
3. **Memory Monitoring**: Track loaded wildcard count
---
## 10. Future Enhancements
### 10.1 Planned Features
1. **LRU Cache**: Automatic eviction of least-used wildcards
2. **Background Preloading**: Preload frequently-used wildcards
3. **Persistent Cache**: Save loaded wildcards across restarts
4. **Usage Statistics**: Track wildcard access patterns
5. **Compression**: Compress infrequently-used wildcards
### 10.2 Performance Improvements
1. **Parallel Loading**: Load multiple wildcards concurrently
2. **Index Structure**: B-tree for faster lookups
3. **Memory Pooling**: Reduce allocation overhead
---
## 11. References
### 11.1 External Documentation
- [Product Requirements Document](WILDCARD_SYSTEM_PRD.md)
- [User Guide](WILDCARD_SYSTEM_OVERVIEW.md)
- [Testing Guide](WILDCARD_TESTING_GUIDE.md)
- [Tutorial](../../ComfyUI-extension-tutorials/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md)
### 11.2 Code References
- **Core Engine**: `modules/impact/wildcards.py`
- **API Server**: `modules/impact/impact_server.py`
- **UI Nodes**: `nodes.py` (ImpactWildcardProcessor, ImpactWildcardEncode)
---
**Document Approval**:
- Engineering Lead: ✅ Approved
- Architecture Review: ✅ Approved
- Security Review: ✅ Approved
**Last Review**: 2025-11-18

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# Wildcard System - Product Requirements Document
**Product**: ComfyUI Impact Pack Wildcard System
**Version**: 2.0 (Depth-Agnostic Matching)
**Status**: Released
**Last Updated**: 2025-11-18
---
## 1. Overview
### 1.1 Product Vision
The Wildcard System provides **dynamic text generation** for AI prompts, enabling users to create rich, varied prompts with minimal manual effort.
### 1.2 Target Users
- **AI Artists**: Creating varied prompts for image generation
- **Content Creators**: Generating diverse text content
- **Game Designers**: Dynamic NPC dialogue and procedural content
- **ComfyUI Users**: Workflow automation with dynamic text
---
## 2. Core Features
> **Note**: For detailed syntax examples and usage guides, see the [ImpactWildcard Tutorial](../../../ComfyUI-extension-tutorials/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md).
### 2.1 Wildcard Syntax
**Basic Wildcards**:
- `__wildcard_name__` - Simple text replacement (e.g., `__flower__` → random flower from flower.txt)
- `__category/subcategory__` - Hierarchical organization with subdirectories (e.g., `__obj/person__`)
- Transitive wildcards - Wildcards can reference other wildcards
- Case-insensitive matching - `__Jewel__` and `__jewel__` are identical
- `*` aggregation pattern (V4.15.1+) - Groups all items from path and subdirectories into one collection
**Quantifiers**:
- `N#__wildcard__` - Repeat wildcard N times
- Example: `5#__wildcards__` expands to `__wildcards__|__wildcards__|__wildcards__|__wildcards__|__wildcards__`
- Can be combined with multi-select: `{2$$, $$5#__wildcards__}`
**Comments**:
- Lines starting with `#` are treated as comments and removed
- Text following a comment is separated by single blank space from text before comment
- Example:
```
first {a|b|c} second # not a comment,
# this is a comment
trailing text
```
Becomes: `first a second # not a comment, trailing text`
**Pattern Matching**:
- `__*/wildcard__` - Depth-agnostic pattern matching at any directory level
- Automatic fallback when direct lookup fails
---
### 2.2 Dynamic Prompts
**Basic Selection**:
- `{option1|option2|option3}` - Random selection from options
- Unlimited nesting: `{a|{d|e|f}|c}` - Nested options are evaluated
- Example: `{blue apple|red {cherry|berry}|green melon}` → `blue apple`, `red cherry`, `red berry`, or `green melon`
- Complex nesting: `1{girl is holding {blue pencil|red __fruit__|colorful __flower__}|boy is riding __vehicle__}`
**Weighted Selection**:
- `{weight::option}` - Control selection probability
- **Syntax**: Weight comes FIRST, then `::`, then the option value
- **Correct**: `{10::common|1::rare}` → 10:1 ratio (≈91% vs ≈9%)
- **Incorrect**: `{common::10|rare::1}` → Will be treated as equal weights (50% vs 50%)
- Weights are normalized: `{5::red|3::green|2::blue}` → 50% red, 30% green, 20% blue
- Unweighted options default to weight 1: `{5::red|green|2::blue}` → 5:1:2 ratio
**Limitations**:
- Weights must be integers or simple decimals (e.g., `5`, `10`, `0.5`)
- Complex decimal weights may cause parsing issues due to multiselect pattern conflicts
- For decimal ratios, prefer integer equivalents: use `{5::a|3::b|2::c}` instead of `{0.5::a|0.3::b|0.2::c}`
**Multi-Select**:
- `{n$$opt1|opt2|opt3}` - Select exactly n items
- `{n1-n2$$opt1|opt2|opt3}` - Select between n1 and n2 items (excess ignored if range exceeds options)
- `{-n$$opt1|opt2|opt3}` - Select between 1 and n items
- **Custom separator**: `{n$$ separator $$opt1|opt2|opt3}`
- Example: `{2$$ and $$red|blue|green}` → "red and blue"
- Example: `{1-2$$ or $$apple|orange|banana}` → "apple" or "apple or orange"
---
### 2.3 ComfyUI Nodes
**ImpactWildcardProcessor**:
- **Purpose**: Browser-level wildcard processing for prompt generation
- **Dual Input Fields**:
- Upper field: Wildcard Prompt (accepts wildcard syntax)
- Lower field: Populated Prompt (displays generated result)
- **Mode Control**:
- **Populate**: Processes wildcards on queue prompt, populates result (read-only)
- **Fixed**: Ignores wildcard prompt, allows manual editing of populated prompt
- **Seed Input**:
- Supports seed-based deterministic generation
- Compatible seed inputs: `ImpactInt`, `Seed (rgthree)` only
- Limitation: Reads superficial input only, does not use execution results from other nodes
- **UI Indicator**:
- 🟢 Full Cache: All wildcards pre-loaded
- 🔵 On-Demand: Shows count of loaded wildcards
**ImpactWildcardEncode**:
- All features of ImpactWildcardProcessor
- **LoRA Loading**: `<lora:name:model_weight:clip_weight>` syntax
- If `clip_weight` omitted, uses same value as `model_weight`
- All loaded LoRAs applied to both `model` and `clip` outputs
- **LoRA Block Weight (LBW)** (requires Inspire Pack):
- Syntax: `<lora:name:model_weight:clip_weight:LBW=spec;>`
- Use `;` as separator within spec, recommended to end with `;`
- Specs without `A=` or `B=` → used in `Lora Loader (Block Weight)` node
- Specs with `A=` or `B=` → parameters for `A` and `B` in loader node
- Examples:
- `<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;>`
- **BREAK Syntax**: Separately encode prompts and connect using `Conditioning (Concat)`
- **Output**: Returns processed conditioning with all LoRAs applied
---
### 2.4 Detailer Integration
Special syntax for Detailer Wildcard nodes (region-specific prompt application).
**Ordering Control** (place at very beginning of prompt):
- `[ASC]` - Ascending order by (x, y) coordinates (left takes precedence, then top)
- `[DSC]` - Descending order by (x, y) coordinates
- `[ASC-SIZE]` - Ascending order by area size
- `[DSC-SIZE]` - Descending order by area size
- `[RND]` - Random order
- Example: `[ASC]\n1girl, blue eyes, smile [SEP]\n1boy, brown eyes [SEP]`
**Area Control**:
- `[SEP]` - Separator for different prompts per detection area (SEG)
- `[SKIP]` - Skip detailing for current SEG
- `[STOP]` - Stop detailing, including current SEG
- `[CONCAT]` - Concatenate wildcard conditioning with positive conditioning (instead of replacing)
**Label-Based Application**:
- `[LAB]` - Apply prompts based on labels (each label appears once)
- `[ALL]` - Prefix that applies to all labels
- Example:
```
[LAB]
[ALL] laugh, detailed eyes
[Female] blue eyes
[Male] brown eyes
```
Female labels get: "laugh, detailed eyes, blue eyes"
Male labels get: "laugh, detailed eyes, brown eyes"
**Complete Example**:
```
[DSC-SIZE]
sun glasses[SEP]
[SKIP][SEP]
blue glasses[SEP]
[STOP]
```
Result: Faces sorted by size descending, largest gets "sun glasses", second largest skipped, third gets "blue glasses", rest not detailed.
---
### 2.5 File Formats
**TXT Files**:
- **Format**: One option per line (comma-separated on single line = one item)
- **Comments**: Lines starting with `#` are comments
- **Encoding**: UTF-8
- **Loading**: Supports on-demand loading (loaded only when used)
- **Subfolder Support**: Use path in wildcard name (e.g., `custom_wildcards/obj/person.txt` → `__obj/person__`)
- **Example** (flower.txt):
```
rose
orchid
iris
carnation
lily
```
**YAML Files** (V4.18.4+):
- **Format**: Nested hierarchical structure with multiple levels
- **Usage**: Keys become wildcard paths (e.g., `astronomy.Celestial-Bodies` → `__astronomy/Celestial-Bodies__`)
- **Loading**: Always pre-loaded at startup (keys exist in file content, not path)
- **Example**:
```yaml
astronomy:
Celestial-Bodies:
- Star
- Planet
surface-swap:
- swap the surfaces for
- replace the surfaces with
```
- **Performance Note**: For large collections with on-demand loading, prefer TXT file structure over YAML
**Wildcard Directories**:
- Default directories: `ComfyUI-Impact-Pack/wildcards/` and `ComfyUI-Impact-Pack/custom_wildcards/`
- Recommendation: Use `custom_wildcards/` to avoid conflicts during updates
- Custom path: Configure via `impact-pack.ini` → `custom_wildcards` setting
---
### 2.6 System Features
**Progressive On-Demand Loading** ⭐:
- **Automatic Mode Detection**: System chooses optimal loading strategy based on collection size
- **Full Cache Mode** (total size < 50MB):
- All wildcards loaded into memory at startup
- Instant access with no load delays
- UI Indicator: 🟢 `Select Wildcard 🟢 Full Cache`
- Startup log: `Using full cache mode.`
- **On-Demand Mode** (total size ≥ 50MB):
- Only metadata scanned at startup (< 1 minute for 10GB+)
- Actual wildcard data loaded progressively as accessed
- Low initial memory (< 100MB)
- UI Indicator: 🔵 `Select Wildcard 🔵 On-Demand: X loaded`
- Startup log: `Using on-demand loading mode (metadata scan only).`
- **Configuration**: Adjust threshold via `impact-pack.ini` → `wildcard_cache_limit_mb = 50`
- **File Type Behavior**:
- TXT files: Full on-demand loading support
- YAML files: Always pre-loaded (keys embedded in content)
- **Refresh Behavior**: Clears all cached data, re-scans directories, re-determines mode
**Depth-Agnostic Matching** ⭐:
- **Automatic Fallback**: When direct lookup fails, searches for pattern matches at any depth
- **Pattern Matching**: Finds keys that end with, start with, or contain the wildcard name
- **Multi-Source Combination**: Combines all matched wildcards into single selection pool
- **Zero Configuration**: Works automatically with any directory structure
- **Performance**: Results cached for subsequent access
**Wildcard Refresh API**:
- `GET /impact/wildcards/refresh` - Reload wildcards without restarting ComfyUI
- Clears all cached data (full cache and on-demand loaded)
- Re-scans wildcard directories
- Re-determines loading mode
**Other APIs**:
- `POST /impact/wildcards` - Process wildcard text with seed
- `GET /impact/wildcards/list` - List all available wildcards
- `GET /impact/wildcards/list/loaded` - Show currently loaded wildcards (on-demand mode)
**Deterministic Generation**:
- Seed-based random selection ensures reproducibility
- Same seed + same wildcard = same result
- Compatible with ImpactInt and Seed(rgthree) nodes
---
## 3. Requirements
### 3.1 Functional Requirements
**FR-1: Wildcard Processing**
- Support all documented syntax patterns
- Deterministic results with seed control
- Up to 100 levels of nested expansion
- Graceful error handling
**FR-2: Dynamic Prompts**
- Random, weighted, and multi-select
- Unlimited nesting depth
- Custom separators
**FR-3: Progressive Loading**
- Automatic mode detection
- On-demand loading for large collections
- Real-time tracking
**FR-4: Depth-Agnostic Matching**
- Automatic fallback pattern matching
- Combine all matched wildcards
- Support any directory structure
**FR-5: ComfyUI Integration**
- ImpactWildcardProcessor node
- ImpactWildcardEncode node with LoRA
- Detailer special syntax
---
### 3.2 Non-Functional Requirements
**NFR-1: Usability**
- Time to first success: < 5 minutes
- Zero configuration for basic use
- Clear error messages
**NFR-2: Reliability**
- 100% deterministic with same seed
- Graceful error handling
- No data loss on refresh
**NFR-3: Compatibility**
- Python 3.8+
- Windows, Linux, macOS
- Backward compatible with v1.x
**NFR-4: Scalability**
- Collections up to 100GB
- Up to 1M wildcard files
- Concurrent multi-user access
---
## 4. Configuration
**File**: `impact-pack.ini` (in ComfyUI-Impact-Pack directory)
```ini
[default]
# Custom wildcard directory (optional)
# Use this to specify additional wildcard directory path
custom_wildcards = /path/to/wildcards
# Cache size limit in MB (default: 50)
# Determines threshold for Full Cache vs On-Demand mode
wildcard_cache_limit_mb = 50
```
**Default Wildcard Directories**:
- `ComfyUI-Impact-Pack/wildcards/` - System wildcards (avoid modifying)
- `ComfyUI-Impact-Pack/custom_wildcards/` - User wildcards (recommended)
- Custom path via `custom_wildcards` setting (optional)
**Configuration Best Practices**:
- No configuration required for basic use
- Use `custom_wildcards/` to avoid conflicts during updates
- Adjust `wildcard_cache_limit_mb` based on system memory and collection size:
- Lower limit → More likely to use on-demand mode (slower first access, lower memory)
- Higher limit → More likely to use full cache mode (faster access, higher memory)
- For large collections (10GB+), consider organizing into subdirectories for better performance
---
## 5. User Workflows
### 5.1 Getting Started
**Goal**: First wildcard in < 5 minutes
1. Create file: `custom_wildcards/flower.txt`
2. Add content (one per line):
```
rose
orchid
iris
carnation
lily
```
3. Use in ImpactWildcardProcessor: `a beautiful __flower__`
4. Set mode to Populate and run queue prompt
5. Result: Random selection like "a beautiful rose"
### 5.2 Reusable Prompt Templates
**Goal**: Save frequently used prompts
1. Create `custom_wildcards/ppos.txt` with:
```
photorealistic:1.4, best quality:1.4
```
2. Use concise prompt: `__ppos__, beautiful nature`
3. Result: "photorealistic:1.4, best quality:1.4, beautiful nature"
### 5.3 Large Collections
**Goal**: Import 10GB+ seamlessly
1. Copy large wildcard collection to directory
2. Start ComfyUI (< 1 minute startup with on-demand mode)
3. Check UI indicator: 🔵 On-Demand mode active
4. Use wildcards immediately (loaded on first access)
5. Subsequent uses are cached for speed
### 5.4 LoRA + Wildcards
**Goal**: Dynamic character with LoRA
1. Create `custom_wildcards/characters.txt`:
```
<lora:char1:1.0:1.0> young girl with blue dress
<lora:char2:1.0:1.0> warrior with armor
<lora:char3:1.0:1.0> mage with robe
```
2. Use ImpactWildcardEncode node
3. Prompt: `__characters__, {day|night} scene, detailed face`
4. Result: Random character with LoRA loaded + random time of day
### 5.5 Multi-Face Detailing
**Goal**: Different prompts for multiple detected faces
1. Create Detailer Wildcard prompt:
```
[DSC-SIZE]
blue eyes, smile[SEP]
brown eyes, serious[SEP]
green eyes, laugh
```
2. Result: Largest face gets "blue eyes, smile", second gets "brown eyes, serious", third gets "green eyes, laugh"
---
## 6. References
### User Documentation
- **[ImpactWildcard Tutorial](../../../ComfyUI-extension-tutorials/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md)** - Complete feature documentation
### Technical Documentation
- **[Design Document](WILDCARD_SYSTEM_DESIGN.md)** - Architecture details
- **[Testing Guide](WILDCARD_TESTING_GUIDE.md)** - Test procedures
---
## Appendix: Glossary
- **Wildcard**: Reusable text snippet (`__name__`)
- **Dynamic Prompt**: Inline options (`{a|b|c}`)
- **Pattern Matching**: Finding wildcards by partial match
- **Depth-Agnostic**: Works with any directory structure
- **On-Demand Loading**: Load data when accessed
- **LoRA**: Low-Rank Adaptation models
- **Detailer**: Node for region-specific processing
---
**Last Updated**: 2025-11-18

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# Wildcard System Testing Guide
Complete testing guide for the ComfyUI Impact Pack wildcard system.
---
## 📋 Table of Contents
1. [Test Overview](#test-overview)
2. [Test Suites](#test-suites)
3. [Quick Start](#quick-start)
4. [Running Tests](#running-tests)
5. [Test Validation](#test-validation)
---
## Test Overview
### Test Statistics
- **Total Tests**: 86 tests across 7 suites
- **Coverage**: 100% of PRD core requirements
- **Pass Rate**: 100%
- **Test Types**: UTF-8, error handling, edge cases, nesting, on-demand, config, dynamic prompts
### Test Structure
```
tests/
├── Test Suites (7 suites, 86 tests)
│ ├── test_encoding.sh # 15 tests - UTF-8 multi-language support
│ ├── test_error_handling.sh # 10 tests - Error recovery and graceful handling
│ ├── test_edge_cases.sh # 20 tests - Boundary conditions and special cases
│ ├── test_deep_nesting.sh # 17 tests - 7-level transitive expansion + pattern matching
│ ├── test_ondemand_loading.sh # 8 tests - Progressive lazy loading with cache limits
│ ├── test_config_quotes.sh # 5 tests - Configuration path handling
│ └── test_dynamic_prompts_full.sh # 11 tests - Weighted/multiselect with statistical validation
│
├── Documentation
│ ├── README.md # Test suite overview
│ └── RUN_ALL_TESTS.md # Execution guide
│
├── Test Samples
│ └── wildcards/samples/ # Test wildcard files
│ ├── level1/.../level7/ # 7-level nesting structure
│ ├── *.txt # Various test wildcards
│ └── 아름다운색.txt # Korean UTF-8 sample
│
└── Utilities
└── restart_test_server.sh # Server management utility
```
---
## Test Suites
### 1. UTF-8 Encoding Tests (15 tests)
**File**: `test_encoding.sh`
**Port**: 8188
**Purpose**: Multi-language support validation
**Test Coverage**:
- Korean text (한글)
- Chinese text (中文)
- Arabic text (العربية)
- Emoji support (🐉🔥⚡)
- Special characters
- Mixed multi-language content
- Case-insensitive Korean matching
**Key Validations**:
- All non-ASCII characters preserved
- UTF-8 encoding consistency
- No character corruption
- Proper string comparison
---
### 2. Error Handling Tests (10 tests)
**File**: `test_error_handling.sh`
**Port**: 8189
**Purpose**: Graceful error recovery
**Test Coverage**:
- Non-existent wildcards
- Missing files
- Circular reference detection (direct and indirect)
- Malformed dynamic prompt syntax
- Deep nesting without crashes
- Invalid quantifiers
**Key Validations**:
- No server crashes
- Clear error messages
- Original text preserved on error
- Circular detection within 100 iterations
---
### 3. Edge Cases Tests (20 tests)
**File**: `test_edge_cases.sh`
**Port**: 8190
**Purpose**: Boundary conditions and special scenarios
**Test Coverage**:
- Empty lines and comments in wildcard files
- Very long lines (>1000 chars)
- Basic wildcard expansion
- Case-insensitive matching
- Quantifiers (1-10 repetitions)
- Pattern matching (`__*/name__`)
**Key Validations**:
- Empty lines filtered correctly
- Comments ignored properly
- Long text handling
- Quantifier accuracy
- Pattern matching at any depth
---
### 4. Deep Nesting Tests (17 tests)
**File**: `test_deep_nesting.sh`
**Port**: 8194
**Purpose**: 7-level transitive expansion and pattern matching
**Test Coverage**:
- Direct level access (Level 1-7)
- Transitive expansion through all levels
- Multiple wildcard nesting
- Mixed depth combinations
- Quantifiers with nesting
- Weighted selection with nesting
- Depth-agnostic pattern matching
**Key Validations**:
- All 7 levels fully expanded
- No unexpanded wildcards remain
- Pattern matching ignores directory depth
- Complex combinations work correctly
**Directory Structure**:
```
samples/level1/level2/level3/level4/level5/level6/level7/
```
---
### 5. On-Demand Loading Tests (8 tests)
**File**: `test_ondemand_loading.sh`
**Port**: 8191
**Purpose**: Progressive lazy loading with configurable cache limits
**Test Coverage**:
- Small cache (1MB) - On-demand mode
- Medium cache (10MB) - Hybrid mode
- Large cache (100MB) - Full cache mode
- Aggressive lazy (0.5MB)
- Various thresholds (5MB, 20MB, 50MB)
**Key Validations**:
- Correct loading mode selection
- Progressive loading functionality
- Cache limit enforcement
- No performance degradation
**Note**: Uses temporary samples in `/tmp/` with auto-cleanup
---
### 6. Config Quotes Tests (5 tests)
**File**: `test_config_quotes.sh`
**Port**: 8192
**Purpose**: Configuration path handling with quotes
**Test Coverage**:
- Paths with single quotes
- Paths with double quotes
- Paths with spaces (quoted)
- Mixed quote scenarios
- Unquoted baseline
**Key Validations**:
- Quotes stripped correctly
- Paths with spaces handled
- Wildcards loaded from quoted paths
---
### 7. Dynamic Prompts Tests (11 tests)
**File**: `test_dynamic_prompts_full.sh`
**Port**: 8193
**Purpose**: Statistical validation of weighted and multiselect features
**Test Coverage**:
- Multiselect (2-5 items) with custom separators
- Weighted selection (various ratios: 10:1, 1:1:1, 5:3:2)
- Nested dynamic prompts
- Basic random selection
- Seed variation validation
**Statistical Validation**:
- 100 iterations for weighted selection
- 20 iterations for multiselect
- Distribution verification (±15% tolerance)
- Duplicate detection
- Separator validation
**Key Validations**:
- Exact item count for multiselect
- No duplicates in multiselect
- Correct separators
- Statistical distribution matches weight ratios
- Nested prompt expansion
---
## Quick Start
### Run All Tests
```bash
cd tests/
bash test_encoding.sh && \
bash test_error_handling.sh && \
bash test_edge_cases.sh && \
bash test_deep_nesting.sh && \
bash test_ondemand_loading.sh && \
bash test_config_quotes.sh && \
bash test_dynamic_prompts_full.sh
```
### Run Individual Suite
```bash
cd tests/
bash test_encoding.sh
```
### Check Test Results
All tests output:
- ✅ PASS - Test succeeded with validation
- ❌ FAIL - Test failed (should not occur)
- ⚠️ WARNING - Partial success or non-critical issue
---
## Running Tests
### Prerequisites
- ComfyUI server must be installable
- Port availability (8188-8194)
- Network access to 127.0.0.1
- Python 3 with json module
### Automatic Server Management
All test suites automatically:
1. Kill any existing server on target port
2. Create temporary configuration file
3. Start ComfyUI server
4. Wait for server ready (up to 60s)
5. Execute tests
6. Clean up (kill server, remove config)
### Test Execution Flow
```
1. Setup
├─ Kill existing server on port
├─ Create impact-pack.ini config
└─ Start ComfyUI server
2. Wait for Ready
├─ Poll server every second
├─ Max 60 seconds timeout
└─ Log tail on failure
3. Execute Tests
├─ Call /impact/wildcards API
├─ Validate responses
└─ Check behavior
4. Cleanup
├─ Kill server process
└─ Remove config file
```
---
## Test Validation
### What Tests Validate
**Behavioral Validation** (Not just "no errors"):
- **Weighted Selection**: Statistical distribution matches weight ratios
- **Multiselect**: Exact count, no duplicates, correct separator
- **Nesting**: All levels fully expanded, no remaining wildcards
- **Pattern Matching**: Depth-agnostic matching works correctly
- **UTF-8**: Character preservation and proper encoding
- **Error Handling**: Graceful recovery with meaningful messages
### Success Criteria
- All 86 tests must pass (100% pass rate)
- No server crashes or hangs
- API responses within expected format
- Statistical distributions within ±15% tolerance
- No unexpanded wildcards in final output
### Validation Examples
**Weighted Selection**:
```bash
# Test 10:1 ratio with 100 iterations
# Expected: ~91% common, ~9% rare
# Actual: Count distribution within ±15%
```
**Multiselect**:
```bash
# Test {2$$, $$red|blue|green}
# Expected: Exactly 2 items, comma-space separator, no duplicates
# Validation: Count words, check separator, detect duplicates
```
**Pattern Matching**:
```bash
# Test __*/dragon__
# Expected: Matches dragon.txt, fantasy/dragon.txt, dragon/fire.txt
# Validation: No unexpanded wildcards remain
```
---
## Troubleshooting
### Common Issues
**Server Fails to Start**:
```bash
# Check log file
tail -20 /tmp/{test_name}_test.log
# Check port availability
lsof -i :8188
# Kill conflicting process
pkill -f "python.*main.py.*--port 8188"
```
**Tests Timeout**:
- Increase wait time in test script (default 60s)
- Check server performance and resources
- Verify network connectivity to 127.0.0.1
**Statistical Tests Fail**:
- Expected for very small sample sizes
- ±15% tolerance accounts for randomness
- Rerun test to verify consistency
**UTF-8 Issues**:
- Ensure terminal supports UTF-8
- Check file encoding: `file -i tests/wildcards/samples/*.txt`
- Verify locale: `locale | grep UTF-8`
---
## Test Maintenance
### Adding New Tests
1. Create new test function in appropriate suite
2. Follow existing test patterns (setup, execute, validate, cleanup)
3. Update test counts in README.md and SUMMARY.md
4. Update this guide with new test description
### Modifying Existing Tests
1. Preserve behavioral validation (not just "no errors")
2. Maintain statistical rigor for dynamic prompt tests
3. Update documentation if test purpose changes
4. Verify all 86 tests still pass after modification
### Test Philosophy
- **Tests validate behavior**, not just execution success
- **Statistical validation** for probabilistic features
- **Real-world scenarios** with production-like setup
- **Comprehensive coverage** of all PRD requirements

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

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custom_nodes/ComfyUI-Impact-Pack/install.py Normal file
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import os
import shutil
import sys
import subprocess
import threading
import locale
import traceback
if sys.argv[0] == 'install.py':
sys.path.append('.') # for portable version
impact_path = os.path.join(os.path.dirname(__file__), "modules")
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)
sys.path.append(comfy_path)
# ---
def handle_stream(stream, is_stdout):
stream.reconfigure(encoding=locale.getpreferredencoding(), errors='replace')
for msg in stream:
if is_stdout:
print(msg, end="", file=sys.stdout)
else:
print(msg, end="", file=sys.stderr)
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, env=env, text=True, bufsize=1)
if handler is None:
handler = handle_stream
stdout_thread = threading.Thread(target=handler, args=(process.stdout, True))
stderr_thread = threading.Thread(target=handler, args=(process.stderr, False))
stdout_thread.start()
stderr_thread.start()
stdout_thread.join()
stderr_thread.join()
return process.wait()
# ---
try:
from torchvision.datasets.utils import download_url
import impact.config
print("### ComfyUI-Impact-Pack: Check dependencies")
def install():
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")
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')):
try:
if not os.path.exists(os.path.join(sam_path, "sam_vit_b_01ec64.pth")):
download_url("https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth", sam_path)
except:
print("[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})")
os.mkdir(onnx_path)
impact.config.write_config()
# Remove legacy subpack
try:
subpack_path = os.path.join(os.path.dirname(__file__), 'impact_subpack')
if os.path.exists(subpack_path):
shutil.rmtree(subpack_path)
print(f"Legacy subpack is detected. '{subpack_path}' is removed.")
subpack_path = os.path.join(os.path.dirname(__file__), 'subpack')
if os.path.exists(subpack_path):
shutil.rmtree(subpack_path)
print(f"Legacy subpack is detected. '{subpack_path}' is removed.")
except:
print(f"ERROT: Failed to delete legacy subpack '{subpack_path}'\nPlease delete the folder after terminate ComfyUI.")
install()
except Exception:
print("[ERROR] ComfyUI-Impact-Pack: Dependency installation has failed. Please install manually.")
traceback.print_exc()

281
custom_nodes/ComfyUI-Impact-Pack/js/common.js Normal file
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import { api } from "../../scripts/api.js";
import { app } from "../../scripts/app.js";
let original_show = app.ui.dialog.show;
export function customAlert(message) {
try {
app.extensionManager.toast.addAlert(message);
}
catch {
alert(message);
}
}
export function isBeforeFrontendVersion(compareVersion) {
try {
const frontendVersion = window['__COMFYUI_FRONTEND_VERSION__'];
if (typeof frontendVersion !== 'string') {
return false;
}
function parseVersion(versionString) {
const parts = versionString.split('.').map(Number);
return parts.length === 3 && parts.every(part => !isNaN(part)) ? parts : null;
}
const currentVersion = parseVersion(frontendVersion);
const comparisonVersion = parseVersion(compareVersion);
if (!currentVersion || !comparisonVersion) {
return false;
}
for (let i = 0; i < 3; i++) {
if (currentVersion[i] > comparisonVersion[i]) {
return false;
} else if (currentVersion[i] < comparisonVersion[i]) {
return true;
}
}
return false;
} catch {
return true;
}
}
function dialog_show_wrapper(html) {
if (typeof html === "string") {
if(html.includes("IMPACT-PACK-SIGNAL: STOP CONTROL BRIDGE")) {
return;
}
this.textElement.innerHTML = html;
} else {
this.textElement.replaceChildren(html);
}
this.element.style.display = "flex";
}
app.ui.dialog.show = dialog_show_wrapper;
function nodeFeedbackHandler(event) {
let nodes = app.graph._nodes_by_id;
let node = nodes[event.detail.node_id];
if(node) {
const w = node.widgets.find((w) => event.detail.widget_name === w.name);
if(w) {
w.value = event.detail.value;
}
}
}
api.addEventListener("impact-node-feedback", nodeFeedbackHandler);
function setMuteState(event) {
let nodes = app.graph._nodes_by_id;
let node = nodes[event.detail.node_id];
if(node) {
if(event.detail.is_active)
node.mode = 0;
else
node.mode = 2;
}
}
api.addEventListener("impact-node-mute-state", setMuteState);
async function bridgeContinue(event) {
let nodes = app.graph._nodes_by_id;
let node = nodes[event.detail.node_id];
if(node) {
const mutes = new Set(event.detail.mutes);
const actives = new Set(event.detail.actives);
const bypasses = new Set(event.detail.bypasses);
for(let i in app.graph._nodes_by_id) {
let this_node = app.graph._nodes_by_id[i];
if(mutes.has(i)) {
this_node.mode = 2;
}
else if(actives.has(i)) {
this_node.mode = 0;
}
else if(bypasses.has(i)) {
this_node.mode = 4;
}
}
await app.queuePrompt(0, 1);
}
}
api.addEventListener("impact-bridge-continue", bridgeContinue);
function addQueue(event) {
app.queuePrompt(0, 1);
}
api.addEventListener("impact-add-queue", addQueue);
function refreshPreview(event) {
let node_id = event.detail.node_id;
let item = event.detail.item;
let img = new Image();
img.src = `/view?filename=${item.filename}&subfolder=${item.subfolder}&type=${item.type}&no-cache=${Date.now()}`;
let node = app.graph._nodes_by_id[node_id];
if(node)
node.imgs = [img];
}
api.addEventListener("impact-preview", refreshPreview);
// ============================================================================
// MaskRectArea Shared Utilities
// ============================================================================
/**
* Reads a numeric value from a connected link by inspecting the origin node widget.
* More reliable than getInputData() in ComfyUI's frontend execution model.
*
* @param {LGraphNode} node - LiteGraph node instance
* @param {string} inputName - Name of the input to read
* @returns {number|null} The numeric value or null if not available
*/
export function readLinkedNumber(node, inputName) {
try {
if (!node || !node.graph || !Array.isArray(node.inputs)) {
return null;
}
const inp = node.inputs.find(i => i && i.name === inputName);
if (!inp || inp.link == null) {
return null;
}
const link = node.graph.links && node.graph.links[inp.link];
if (!link) {
return null;
}
const originNode = node.graph.getNodeById
? node.graph.getNodeById(link.origin_id)
: null;
if (!originNode || !Array.isArray(originNode.widgets) || originNode.widgets.length === 0) {
return null;
}
const w = originNode.widgets.find(ww => ww && ww.name === "value")
|| originNode.widgets[0];
const v = w ? w.value : null;
return (typeof v === "number") ? v : null;
} catch (e) {
return null;
}
}
/**
* Generates a color based on percentage using HSL color space.
*
* @param {number} percent - Value between 0 and 1
* @param {string} alpha - Hex alpha value (e.g., "ff", "80")
* @returns {string} Hex color string with alpha (e.g., "#ff8040ff")
*/
export 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');
};
return `#${f(0)}${f(8)}${f(4)}${alpha}`;
}
/**
* Computes and adjusts canvas size for preview widgets.
*
* @param {LGraphNode} node - LiteGraph node instance
* @param {[number, number]} size - [width, height] array
* @param {number} minHeight - Minimum canvas height (REQUIRED)
* @param {number} minWidth - Minimum canvas width (REQUIRED)
* @returns {void}
*/
export function computeCanvasSize(node, size, minHeight, minWidth) {
// Validate required parameters
if (typeof minHeight !== 'number' || typeof minWidth !== 'number') {
console.warn('[computeCanvasSize] minHeight and minWidth are required parameters');
return;
}
// Null safety check for widgets array
if (!node.widgets?.length || node.widgets[0].last_y == null) {
return;
}
// LiteGraph global availability check
const NODE_WIDGET_HEIGHT = (typeof LiteGraph !== 'undefined' && LiteGraph.NODE_WIDGET_HEIGHT)
? LiteGraph.NODE_WIDGET_HEIGHT
: 20;
let y = node.widgets[0].last_y + 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 += NODE_WIDGET_HEIGHT + 5;
}
}
}
// Ensure there is enough vertical space
freeSpace -= widgetHeight;
// Clamp minimum canvas height
if (freeSpace < minHeight) {
freeSpace = minHeight;
}
// Allow both grow and shrink to fit content
const targetHeight = y + widgetHeight + freeSpace;
if (node.size[1] !== targetHeight) {
node.size[1] = targetHeight;
node.graph.setDirtyCanvas(true);
}
// Ensure the node width meets the minimum width requirement
if (node.size[0] < minWidth) {
node.size[0] = minWidth;
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 += NODE_WIDGET_HEIGHT + 4;
}
}
node.canvasHeight = freeSpace;
}

229
custom_nodes/ComfyUI-Impact-Pack/js/impact-image-util.js Normal file
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import { ComfyApp, app } from "../../scripts/app.js";
import { api } from "../../scripts/api.js";
function load_image(str) {
let base64String = canvas.toDataURL('image/png');
let img = new Image();
img.src = base64String;
}
function getFileItem(baseType, path) {
try {
let pathType = baseType;
if (path.endsWith("[output]")) {
pathType = "output";
path = path.slice(0, -9);
} else if (path.endsWith("[input]")) {
pathType = "input";
path = path.slice(0, -8);
} else if (path.endsWith("[temp]")) {
pathType = "temp";
path = path.slice(0, -7);
}
const subfolder = path.substring(0, path.lastIndexOf('/'));
const filename = path.substring(path.lastIndexOf('/') + 1);
return {
filename: filename,
subfolder: subfolder,
type: pathType
};
}
catch(exception) {
return null;
}
}
async function loadImageFromUrl(image, node_id, v, need_to_load) {
let item = getFileItem('temp', v);
if(item) {
let params = `?node_id=${node_id}&filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`;
let res = await api.fetchApi('/impact/set/pb_id_image'+params, { cache: "no-store" });
if(res.status == 200) {
let pb_id = await res.text();
if(need_to_load) {;
image.src = api.apiURL(`/view?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`);
}
return pb_id;
}
else {
return `$${node_id}-0`;
}
}
else {
return `$${node_id}-0`;
}
}
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 = api.apiURL(`/view?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`);
return true;
}
return false;
}
app.registerExtension({
name: "Comfy.Impact.img",
nodeCreated(node, app) {
if(node.comfyClass == "PreviewBridge" || node.comfyClass == "PreviewBridgeLatent") {
let w = node.widgets.find(obj => obj.name === 'image');
node._imgs = [new Image()];
node.imageIndex = 0;
Object.defineProperty(w, 'value', {
async set(v) {
if(w._lock)
return;
const stackTrace = new Error().stack;
if(stackTrace.includes('presetText.js'))
return;
var image = new Image();
if(v && v.constructor == String && v.startsWith('$')) {
// from node feedback
let need_to_load = node._imgs[0].src == '';
if(await loadImageFromId(image, v, need_to_load)) {
w._value = v;
if(node._imgs[0].src == '') {
node._imgs = [image];
}
}
else {
w._value = `$${node.id}-0`;
}
}
else {
// from clipspace
w._lock = true;
w._value = await loadImageFromUrl(image, node.id, v, false);
w._lock = false;
}
},
get() {
if(w._value == undefined) {
w._value = `$${node.id}-0`;
}
return w._value;
}
});
Object.defineProperty(node, 'imgs', {
set(v) {
const stackTrace = new Error().stack;
if(v && v.length == 0)
return;
else if(stackTrace.includes('pasteFromClipspace')) {
let sp = new URLSearchParams(v[0].src.split("?")[1]);
let str = "";
if(sp.get('subfolder')) {
str += sp.get('subfolder') + '/';
}
str += `${sp.get("filename")} [${sp.get("type")}]`;
w.value = str;
}
node._imgs = v;
},
get() {
return node._imgs;
}
});
}
if(node.comfyClass == "ImageReceiver") {
let path_widget = node.widgets.find(obj => obj.name === 'image');
let w = node.widgets.find(obj => obj.name === 'image_data');
let stw_widget = node.widgets.find(obj => obj.name === 'save_to_workflow');
w._value = "";
Object.defineProperty(w, 'value', {
set(v) {
if(v != '[IMAGE DATA]')
w._value = v;
},
get() {
const stackTrace = new Error().stack;
if(!stackTrace.includes('draw') && !stackTrace.includes('graphToPrompt') && stackTrace.includes('app.js')) {
return "[IMAGE DATA]";
}
else {
if(stw_widget.value)
return w._value;
else
return "";
}
}
});
let set_img_act = (v) => {
node._img = v;
var canvas = document.createElement('canvas');
canvas.width = v[0].width;
canvas.height = v[0].height;
var context = canvas.getContext('2d');
context.drawImage(v[0], 0, 0, v[0].width, v[0].height);
var base64Image = canvas.toDataURL('image/png');
w.value = base64Image;
};
Object.defineProperty(node, 'imgs', {
set(v) {
if (v && !v[0].complete) {
let orig_onload = v[0].onload;
v[0].onload = function(v2) {
if(orig_onload)
orig_onload();
set_img_act(v);
};
}
else {
set_img_act(v);
}
},
get() {
if(this._img == undefined && w.value != '') {
this._img = [new Image()];
if(stw_widget.value && w.value != '[IMAGE DATA]')
this._img[0].src = w.value;
}
else if(this._img == undefined && path_widget.value) {
let image = new Image();
image.src = path_widget.value;
try {
let item = getFileItem('temp', path_widget.value);
let params = `?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`;
let res = api.fetchApi('/view/validate'+params, { cache: "no-store" }).then(response => response);
if(res.status == 200) {
image.src = api.apiURL('/view'+params);
}
this._img = [new Image()]; // placeholder
image.onload = function(v) {
set_img_act([image]);
};
}
catch {
}
}
return this._img;
}
});
}
}
})

988
custom_nodes/ComfyUI-Impact-Pack/js/impact-pack.js Normal file
View File

@@ -0,0 +1,988 @@
import { ComfyApp, app } from "../../scripts/app.js";
import { ComfyDialog, $el } from "../../scripts/ui.js";
import { api } from "../../scripts/api.js";
import { customAlert, isBeforeFrontendVersion } from "./common.js";
const is_legacy_front = () => isBeforeFrontendVersion('1.16.9');
if(is_legacy_front()) {
customAlert("An outdated version(<1.16.9) of the `comfyui-frontend-package` is installed. It is not compatible with the current version of the Impact Pack.");
}
let wildcards_list = [];
let wildcard_status = {
on_demand_mode: false,
total_available: 0,
loaded_count: 0,
last_update: null
};
async function load_wildcards() {
let res = await api.fetchApi('/impact/wildcards/list');
let data = await res.json();
wildcards_list = data.data;
}
async function load_wildcard_status() {
try {
let res = await api.fetchApi('/impact/wildcards/list/loaded');
let data = await res.json();
wildcard_status = {
on_demand_mode: data.on_demand_mode || false,
total_available: data.total_available || 0,
loaded_count: data.data ? data.data.length : 0,
last_update: new Date()
};
} catch (error) {
console.error('Failed to load wildcard status:', error);
}
}
export function get_wildcard_label() {
if (wildcard_status.on_demand_mode) {
return `Select Wildcard 🔵 On-Demand: ${wildcard_status.loaded_count} loaded`;
} else {
return `Select Wildcard 🟢 Full Cache`;
}
}
export function is_wildcard_label(value) {
// Check if value is a label (not an actual wildcard selection)
return value === "Select the Wildcard to add to the text" ||
value.startsWith("Select Wildcard 🔵 On-Demand:") ||
value === "Select Wildcard 🟢 Full Cache";
}
Promise.all([load_wildcards(), load_wildcard_status()]);
export function get_wildcards_list() {
return wildcards_list;
}
export { load_wildcard_status };
// temporary implementation (copying from https://github.com/pythongosssss/ComfyUI-WD14-Tagger)
// I think this should be included into master!!
class ImpactProgressBadge {
constructor() {
if (!window.__progress_badge__) {
window.__progress_badge__ = Symbol("__impact_progress_badge__");
}
this.symbol = window.__progress_badge__;
}
getState(node) {
return node[this.symbol] || {};
}
setState(node, state) {
node[this.symbol] = state;
app.canvas.setDirty(true);
}
addStatusHandler(nodeType) {
if (nodeType[this.symbol]?.statusTagHandler) {
return;
}
if (!nodeType[this.symbol]) {
nodeType[this.symbol] = {};
}
nodeType[this.symbol] = {
statusTagHandler: true,
};
api.addEventListener("impact/update_status", ({ detail }) => {
let { node, progress, text } = detail;
const n = app.graph.getNodeById(+(node || app.runningNodeId));
if (!n) return;
const state = this.getState(n);
state.status = Object.assign(state.status || {}, { progress: text ? progress : null, text: text || null });
this.setState(n, state);
});
const self = this;
const onDrawForeground = nodeType.prototype.onDrawForeground;
nodeType.prototype.onDrawForeground = function (ctx) {
const r = onDrawForeground?.apply?.(this, arguments);
const state = self.getState(this);
if (!state?.status?.text) {
return r;
}
const { fgColor, bgColor, text, progress, progressColor } = { ...state.status };
ctx.save();
ctx.font = "12px sans-serif";
const sz = ctx.measureText(text);
ctx.fillStyle = bgColor || "dodgerblue";
ctx.beginPath();
ctx.roundRect(0, -LiteGraph.NODE_TITLE_HEIGHT - 20, sz.width + 12, 20, 5);
ctx.fill();
if (progress) {
ctx.fillStyle = progressColor || "green";
ctx.beginPath();
ctx.roundRect(0, -LiteGraph.NODE_TITLE_HEIGHT - 20, (sz.width + 12) * progress, 20, 5);
ctx.fill();
}
ctx.fillStyle = fgColor || "#fff";
ctx.fillText(text, 6, -LiteGraph.NODE_TITLE_HEIGHT - 6);
ctx.restore();
return r;
};
}
}
const input_tracking = {};
const input_dirty = {};
const output_tracking = {};
function progressExecuteHandler(event) {
if(event.detail?.output?.aux){
const id = event.detail.node;
if(input_tracking.hasOwnProperty(id)) {
if(input_tracking.hasOwnProperty(id) && input_tracking[id][0] != event.detail.output.aux[0]) {
input_dirty[id] = true;
}
else{
}
}
input_tracking[id] = event.detail.output.aux;
}
}
function imgSendHandler(event) {
if(event.detail.images.length > 0){
let data = event.detail.images[0];
let filename = `${data.filename} [${data.type}]`;
let nodes = app.graph._nodes;
for(let i in nodes) {
if(nodes[i].type == 'ImageReceiver') {
let is_linked = false;
if(nodes[i].widgets[1].type == 'converted-widget') {
for(let j in nodes[i].inputs) {
let input = nodes[i].inputs[j];
if(input.name === 'link_id') {
if(input.link) {
let src_node = app.graph._nodes_by_id[app.graph.links[input.link].origin_id];
if(src_node.type == 'ImpactInt' || src_node.type == 'PrimitiveNode') {
is_linked = true;
}
}
break;
}
}
}
else if(nodes[i].widgets[1].value == event.detail.link_id) {
is_linked = true;
}
if(is_linked) {
if(data.subfolder)
nodes[i].widgets[0].value = `${data.subfolder}/${data.filename} [${data.type}]`;
else
nodes[i].widgets[0].value = `${data.filename} [${data.type}]`;
let img = new Image();
img.onload = (event) => {
nodes[i].imgs = [img];
nodes[i].size[1] = Math.max(200, nodes[i].size[1]);
app.canvas.setDirty(true);
};
img.src = `/view?filename=${data.filename}&type=${data.type}&subfolder=${data.subfolder}`+app.getPreviewFormatParam();
}
}
}
}
}
function latentSendHandler(event) {
if(event.detail.images.length > 0){
let data = event.detail.images[0];
let filename = `${data.filename} [${data.type}]`;
let nodes = app.graph._nodes;
for(let i in nodes) {
if(nodes[i].type == 'LatentReceiver') {
if(nodes[i].widgets[1].value == event.detail.link_id) {
if(data.subfolder)
nodes[i].widgets[0].value = `${data.subfolder}/${data.filename} [${data.type}]`;
else
nodes[i].widgets[0].value = `${data.filename} [${data.type}]`;
let img = new Image();
img.src = `/view?filename=${data.filename}&type=${data.type}&subfolder=${data.subfolder}`+app.getPreviewFormatParam();
nodes[i].imgs = [img];
nodes[i].size[1] = Math.max(200, nodes[i].size[1]);
}
}
}
}
}
function valueSendHandler(event) {
let nodes = app.graph._nodes;
for(let i in nodes) {
if(nodes[i].type == 'ImpactValueReceiver') {
if(nodes[i].widgets[2].value == event.detail.link_id) {
nodes[i].widgets[1].value = event.detail.value;
let typ = typeof event.detail.value;
if(typ == 'string') {
nodes[i].widgets[0].value = "STRING";
}
else if(typ == "boolean") {
nodes[i].widgets[0].value = "BOOLEAN";
}
else if(typ != "number") {
nodes[i].widgets[0].value = typeof event.detail.value;
}
else if(Number.isInteger(event.detail.value)) {
nodes[i].widgets[0].value = "INT";
}
else {
nodes[i].widgets[0].value = "FLOAT";
}
}
}
}
}
const impactProgressBadge = new ImpactProgressBadge();
api.addEventListener("stop-iteration", () => {
document.getElementById("autoQueueCheckbox").checked = false;
});
api.addEventListener("value-send", valueSendHandler);
api.addEventListener("img-send", imgSendHandler);
api.addEventListener("latent-send", latentSendHandler);
api.addEventListener("executed", progressExecuteHandler);
// Update wildcard status after workflow execution (on-demand mode)
api.addEventListener("executed", async (event) => {
if (wildcard_status.on_demand_mode) {
await load_wildcard_status();
await load_wildcards();
app.canvas.setDirty(true);
}
});
app.registerExtension({
name: "Comfy.Impack",
commands: [
{
id: 'refresh-impact-wildcard',
label: 'Impact: Refresh Wildcard',
function: async () => {
await api.fetchApi('/impact/wildcards/refresh');
await Promise.all([load_wildcards(), load_wildcard_status()]);
app.extensionManager.toast.add({
severity: 'info',
summary: 'Refreshed!',
detail: 'Impact Wildcard List is refreshed!!',
life: 3000
});
}
}
],
menuCommands: [
{
path: ['Edit'],
commands: ['refresh-impact-wildcard']
}
],
loadedGraphNode(node, app) {
if (node.comfyClass == "MaskPainter") {
input_dirty[node.id + ""] = true;
}
},
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (nodeData.name == "IterativeLatentUpscale" || nodeData.name == "IterativeImageUpscale"
|| nodeData.name == "RegionalSampler"|| nodeData.name == "RegionalSamplerAdvanced") {
impactProgressBadge.addStatusHandler(nodeType);
}
if(nodeData.name == "ImpactControlBridge") {
const onConnectionsChange = nodeType.prototype.onConnectionsChange;
nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
if(index != 0 || !link_info || this.inputs[0].type != '*')
return;
// assign type
let slot_type = '*';
if(type == 2) {
slot_type = link_info.type;
}
else {
const node = app.graph.getNodeById(link_info.origin_id);
slot_type = node?.outputs[link_info.origin_slot]?.type;
}
this.inputs[0].type = slot_type;
this.outputs[0].type = slot_type;
this.outputs[0].label = slot_type;
}
}
if(nodeData.name == "ImpactConditionalBranch" || nodeData.name == "ImpactConditionalBranchSelMode") {
const onConnectionsChange = nodeType.prototype.onConnectionsChange;
nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
if(!link_info || this.inputs[0].type != '*')
return;
if(index >= 2)
return;
// assign type
let slot_type = '*';
if(type == 2) {
slot_type = link_info.type;
}
else {
const node = app.graph.getNodeById(link_info.origin_id);
slot_type = node?.outputs[link_info.origin_slot].type;
}
this.inputs[0].type = slot_type;
this.inputs[1].type = slot_type;
this.outputs[0].type = slot_type;
this.outputs[0].label = slot_type;
}
}
if(nodeData.name == "ImpactCompare") {
const onConnectionsChange = nodeType.prototype.onConnectionsChange;
nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
if(!link_info || this.inputs[0].type != '*' || type == 2)
return;
// assign type
const node = app.graph.getNodeById(link_info.origin_id);
let slot_type = node?.outputs[link_info.origin_slot].type;
this.inputs[0].type = slot_type;
this.inputs[1].type = slot_type;
}
}
if(nodeData.name == "ImpactSelectNthItemOfAnyList") {
const onConnectionsChange = nodeType.prototype.onConnectionsChange;
nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
if(!link_info || this.inputs[0].type != '*')
return;
if(index >= 2)
return;
// assign type
let slot_type = '*';
if(type == 2) {
slot_type = link_info.type;
}
else {
const node = app.graph.getNodeById(link_info.origin_id);
slot_type = node?.outputs[link_info.origin_slot].type;
}
this.inputs[0].type = slot_type;
this.outputs[0].type = slot_type;
this.outputs[0].label = slot_type;
}
}
if(nodeData.name === 'ImpactInversedSwitch') {
nodeData.output = ['*'];
nodeData.output_is_list = [false];
nodeData.output_name = ['output1'];
const onConnectionsChange = nodeType.prototype.onConnectionsChange;
nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
if(!link_info)
return;
// HOTFIX: subgraph
const stackTrace = new Error().stack;
if(stackTrace.includes('convertToSubgraph') || stackTrace.includes('Subgraph.configure')) {
return;
}
if(type == 2) {
// connect output
if(connected){
if(app.graph._nodes_by_id[link_info.target_id]?.type == 'Reroute') {
app.graph._nodes_by_id[link_info.target_id].disconnectInput(link_info.target_slot);
}
if(this.outputs[0].type == '*'){
if(link_info.type == '*' && app.graph.getNodeById(link_info.target_id).slots[link_info.target_slot].type != '*') {
app.graph._nodes_by_id[link_info.target_id].disconnectInput(link_info.target_slot);
}
else {
// propagate type
this.outputs[0].type = link_info.type;
this.outputs[0].name = link_info.type;
for(let i in this.inputs) {
if(this.inputs[i].name != 'select')
this.inputs[i].type = link_info.type;
}
}
}
}
}
else {
if(app.graph._nodes_by_id[link_info.origin_id]?.type == 'Reroute')
this.disconnectInput(link_info.target_slot);
// connect input
if(this.inputs[0].type == '*'){
const node = app.graph.getNodeById(link_info.origin_id);
let origin_type = node?.outputs[link_info.origin_slot]?.type;
if(origin_type==undefined) {
return; // fallback
}
if(origin_type == '*' && app.graph.getNodeById(link_info.origin_id).slots[link_info.origin_slot].type != '*') {
this.disconnectInput(link_info.target_slot);
return;
}
for(let i in this.inputs) {
if(this.inputs[i].name != 'select')
this.inputs[i].type = origin_type;
}
this.outputs[0].type = origin_type;
this.outputs[0].name = 'output1';
}
return;
}
if (!connected && this.outputs.length > 1) {
const stackTrace = new Error().stack;
if(
!stackTrace.includes('LGraphNode.prototype.connect') && // for touch device
!stackTrace.includes('LGraphNode.connect') && // for mouse device
!stackTrace.includes('loadGraphData')) {
if(this.outputs[link_info.origin_slot].links.length == 0) {
this.removeOutput(link_info.origin_slot);
}
}
}
let slot_i = 1;
for (let i = 0; i < this.outputs.length; i++) {
this.outputs[i].name = `output${slot_i}`
if (this.outputs[i].slot_index === undefined) {
this.outputs[i].slot_index = i;
}
slot_i++;
}
if(connected) {
// NOTE: node.slot_index is different with link_info.origin_slot
let last_slot_index = this.outputs.length - 1;
if (last_slot_index == link_info.origin_slot) {
this.addOutput(`output${slot_i}`, this.outputs[0].type);
}
}
let select_slot = this.inputs.find(x => x.name == "select");
if(this.widgets?.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)
this.widgets[0].value = 1;
}
}
}
if (nodeData.name === 'ImpactMakeImageList' || nodeData.name === 'ImpactMakeImageBatch' ||
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') {
var input_name = "input";
switch(nodeData.name) {
case 'ImpactMakeImageList':
case 'ImpactMakeImageBatch':
input_name = "image";
break;
case 'ImpactMakeMaskList':
case 'ImpactMakeMaskBatch':
input_name = "mask";
break;
case 'ImpactMakeAnyList':
input_name = "value";
break;
case 'ImpactSEGSConcat':
input_name = "segs";
break;
case 'CombineRegionalPrompts':
input_name = "regional_prompts";
break;
case 'ImpactCombineConditionings':
case 'ImpactConcatConditionings':
input_name = "conditioning";
break;
case 'LatentSwitch':
input_name = "input";
break;
case 'SEGSSwitch':
input_name = "input";
break;
case 'ImpactSwitch':
input_name = "input";
}
const onConnectionsChange = nodeType.prototype.onConnectionsChange;
nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
const stackTrace = new Error().stack;
// HOTFIX: subgraph
if(stackTrace.includes('convertToSubgraph') || stackTrace.includes('Subgraph.configure')) {
return;
}
if(stackTrace.includes('loadGraphData')) {
if(this.widgets?.[0]) {
this.widgets[0].options.max = this.inputs.length-3;
this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max);
}
return;
}
if(stackTrace.includes('pasteFromClipboard')) {
if(this.widgets?.[0]) {
this.widgets[0].options.max = this.inputs.length-3;
this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max);
}
return;
}
if(!link_info)
return;
if(type == 2) {
// connect output
if(connected && index == 0){
if(nodeData.name == 'ImpactSwitch' && app.graph._nodes_by_id[link_info.target_id]?.type == 'Reroute') {
app.graph._nodes_by_id[link_info.target_id].disconnectInput(link_info.target_slot);
}
if(this.outputs[0].type == '*'){
if(link_info.type == '*' && app.graph.getNodeById(link_info.target_id).slots[link_info.target_slot].type != '*') {
app.graph._nodes_by_id[link_info.target_id].disconnectInput(link_info.target_slot);
}
else {
// propagate type
this.outputs[0].type = link_info.type;
this.outputs[0].label = link_info.type;
this.outputs[0].name = link_info.type;
for(let i in this.inputs) {
let input_i = this.inputs[i];
if(input_i.name != 'select' && input_i.name != 'sel_mode')
input_i.type = link_info.type;
}
}
}
}
return;
}
else {
if(nodeData.name == 'ImpactSwitch' && app.graph._nodes_by_id[link_info.origin_id]?.type == 'Reroute')
this.disconnectInput(link_info.target_slot);
// connect input
if(this.inputs[index].name == 'select' || this.inputs[index].name == 'sel_mode')
return;
if(this.inputs[0].type == '*'){
const node = app.graph.getNodeById(link_info.origin_id);
// NOTE: node is undefined when subgraph editing mode
if(node) {
let origin_type = node.outputs[link_info.origin_slot]?.type;
if(link_info.target_slot == 0 && this.inputs.length > 3) { // NOTE: widgets are regarded as input since new front
origin_type = this.inputs[1].type;
node.connect(link_info.origin_slot, node.id, 'input1');
}
if(origin_type == '*' && app.graph.getNodeById(link_info.origin_id).slots[link_info.origin_slot].type != '*') {
this.disconnectInput(link_info.target_slot);
return;
}
for(let i in this.inputs) {
let input_i = this.inputs[i];
if(input_i.name != 'select' && input_i.name != 'sel_mode')
input_i.type = origin_type;
}
this.outputs[0].type = origin_type;
this.outputs[0].label = origin_type;
this.outputs[0].name = origin_type;
}
}
}
let widget_count = 0;
if(nodeData.name == 'ImpactSwitch' || nodeData.name == 'LatentSwitch' || nodeData.name == 'SEGSSwitch') {
widget_count += 1;
}
if (!connected && (this.inputs.length > widget_count+1)) {
if(
!stackTrace.includes('LGraphNode.prototype.connect') && // for touch device
!stackTrace.includes('LGraphNode.connect') && // for mouse device
!stackTrace.includes('loadGraphData') &&
this.inputs[index].name != 'select') {
this.removeInput(index);
}
}
let slot_i = 1;
for (let i = 0; i < this.inputs.length; i++) {
let input_i = this.inputs[i];
if(input_i.name != 'select'&& input_i.name != 'sel_mode') {
input_i.name = `${input_name}${slot_i}`
slot_i++;
}
}
if(connected) {
this.addInput(`${input_name}${slot_i}`, this.outputs[0].type);
}
if(this.widgets?.[0]) {
this.widgets[0].options.max = this.inputs.length-3;
this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max);
}
}
}
},
nodeCreated(node, app) {
if(node.comfyClass == "MaskPainter") {
node.addWidget("button", "Edit mask", null, () => {
ComfyApp.copyToClipspace(node);
ComfyApp.clipspace_return_node = node;
ComfyApp.open_maskeditor();
});
}
switch(node.comfyClass) {
case "ToDetailerPipe":
case "ToDetailerPipeSDXL":
case "BasicPipeToDetailerPipe":
case "BasicPipeToDetailerPipeSDXL":
case "EditDetailerPipe":
case "FaceDetailer":
case "DetailerForEach":
case "DetailerForEachDebug":
case "DetailerForEachPipe":
case "DetailerForEachDebugPipe":
{
for(let i in node.widgets) {
let widget = node.widgets[i];
if(widget.type === "customtext") {
widget.dynamicPrompts = false;
widget.inputEl.placeholder = "wildcard spec: if kept empty, this option will be ignored";
widget.serializeValue = () => {
return node.widgets[i].value;
};
}
}
}
break;
}
if(node.comfyClass == "ImpactSEGSLabelFilter" || node.comfyClass == "SEGSLabelFilterDetailerHookProvider") {
node.widgets[0].callback = (value, canvas, node, pos, e) => {
if(node) {
if(node.widgets[1].value.trim() != "" && !node.widgets[1].value.trim().endsWith(","))
node.widgets[1].value += ", "
node.widgets[1].value += value;
if(node.widgets_values)
node.widgets_values[1] = node.widgets[1].value;
}
}
Object.defineProperty(node.widgets[0], "value", {
set: (value) => {
node._value = value;
},
get: () => {
return node._value;
}
});
}
if(node.comfyClass == "UltralyticsDetectorProvider") {
let model_name_widget = node.widgets.find((w) => w.name === "model_name");
let orig_draw = node.onDrawForeground;
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');
if(!is_seg) {
var slot_pos = new Float32Array(2);
var pos = node.getConnectionPos(false, 1, slot_pos);
pos[0] -= node.pos[0] - 10;
pos[1] -= node.pos[1];
ctx.beginPath();
ctx.strokeStyle = "red";
ctx.lineWidth = 4;
ctx.moveTo(pos[0] - 5, pos[1] - 5);
ctx.lineTo(pos[0] + 5, pos[1] + 5);
ctx.moveTo(pos[0] + 5, pos[1] - 5);
ctx.lineTo(pos[0] - 5, pos[1] + 5);
ctx.stroke();
}
}
}
if(
node.comfyClass == "ImpactWildcardEncode" || node.comfyClass == "ImpactWildcardProcessor"
|| node.comfyClass == "ToDetailerPipe" || node.comfyClass == "ToDetailerPipeSDXL"
|| node.comfyClass == "EditDetailerPipe" || node.comfyClass == "EditDetailerPipeSDXL"
|| node.comfyClass == "BasicPipeToDetailerPipe" || node.comfyClass == "BasicPipeToDetailerPipeSDXL") {
node._value = "Select the LoRA to add to the text";
node._wvalue = "Select the Wildcard to add to the text";
var tbox_id = 0;
var combo_id = 3;
var has_lora = true;
switch(node.comfyClass){
case "ImpactWildcardEncode":
tbox_id = 0;
combo_id = 3;
break;
case "ImpactWildcardProcessor":
tbox_id = 0;
combo_id = 4;
has_lora = false;
break;
case "ToDetailerPipe":
case "ToDetailerPipeSDXL":
case "EditDetailerPipe":
case "EditDetailerPipeSDXL":
case "BasicPipeToDetailerPipe":
case "BasicPipeToDetailerPipeSDXL":
tbox_id = 0;
combo_id = 1;
break;
}
node.widgets[combo_id+1].callback = async (value, canvas, node, pos, e) => {
if(node) {
if(node.widgets[tbox_id].value != '')
node.widgets[tbox_id].value += ', '
node.widgets[tbox_id].value += node._wildcard_value;
// Reload wildcard status to update loaded count
if (wildcard_status.on_demand_mode) {
await load_wildcard_status();
await load_wildcards();
app.canvas.setDirty(true);
}
}
}
Object.defineProperty(node.widgets[combo_id+1], "value", {
set: (value) => {
if (!is_wildcard_label(value))
node._wildcard_value = value;
},
get: () => { return get_wildcard_label(); }
});
Object.defineProperty(node.widgets[combo_id+1].options, "values", {
set: (x) => {},
get: () => {
return wildcards_list;
}
});
if(has_lora) {
node.widgets[combo_id].callback = (value, canvas, node, pos, e) => {
if(node) {
let lora_name = node._value;
if(lora_name.endsWith('.safetensors')) {
lora_name = lora_name.slice(0, -12);
}
node.widgets[tbox_id].value += `<lora:${lora_name}>`;
if(node.widgets_values) {
node.widgets_values[tbox_id] = node.widgets[tbox_id].value;
}
}
}
Object.defineProperty(node.widgets[combo_id], "value", {
set: (value) => {
if (value !== "Select the LoRA to add to the text")
node._value = value;
},
get: () => { return "Select the LoRA to add to the text"; }
});
}
// Preventing validation errors from occurring in any situation.
if(has_lora) {
node.widgets[combo_id].serializeValue = () => { return "Select the LoRA to add to the text"; }
}
node.widgets[combo_id+1].serializeValue = () => { return "Select the Wildcard to add to the text"; }
}
if(node.comfyClass == "ImpactWildcardProcessor" || node.comfyClass == "ImpactWildcardEncode") {
node.widgets[0].inputEl.placeholder = "Wildcard Prompt (User input)";
node.widgets[1].inputEl.placeholder = "Populated Prompt (Will be generated automatically)";
node.widgets[1].inputEl.disabled = true;
const populated_text_widget = node.widgets.find((w) => w.name == 'populated_text');
const mode_widget = node.widgets.find((w) => w.name == 'mode');
// mode combo
Object.defineProperty(mode_widget, "value", {
set: (value) => {
if(value == true)
node._mode_value = "populate";
else if(value == false)
node._mode_value = "fixed";
else
node._mode_value = value; // combo value
populated_text_widget.inputEl.disabled = node._mode_value == 'populate';
},
get: () => {
if(node._mode_value != undefined)
return node._mode_value;
else
return 'populate';
}
});
}
if (node.comfyClass == "MaskPainter") {
node.widgets[0].value = '#placeholder';
Object.defineProperty(node, "images", {
set: function(value) {
node._images = value;
},
get: function() {
const id = node.id+"";
if(node.widgets[0].value != '#placeholder') {
var need_invalidate = false;
if(input_dirty.hasOwnProperty(id) && input_dirty[id]) {
node.widgets[0].value = {...input_tracking[id][1]};
input_dirty[id] = false;
need_invalidate = true
this._images = app.nodeOutputs[id].images;
}
let filename = app.nodeOutputs[id]['aux'][1][0]['filename'];
let subfolder = app.nodeOutputs[id]['aux'][1][0]['subfolder'];
let type = app.nodeOutputs[id]['aux'][1][0]['type'];
let item =
{
image_hash: app.nodeOutputs[id]['aux'][0],
forward_filename: app.nodeOutputs[id]['aux'][1][0]['filename'],
forward_subfolder: app.nodeOutputs[id]['aux'][1][0]['subfolder'],
forward_type: app.nodeOutputs[id]['aux'][1][0]['type']
};
if(node._images) {
app.nodeOutputs[id].images = [{
...node._images[0],
...item
}];
node.widgets[0].value =
{
...node._images[0],
...item
};
}
else {
app.nodeOutputs[id].images = [{
...item
}];
node.widgets[0].value =
{
...item
};
}
if(need_invalidate) {
Promise.all(
app.nodeOutputs[id].images.map((src) => {
return new Promise((r) => {
const img = new Image();
img.onload = () => r(img);
img.onerror = () => r(null);
img.src = "/view?" + new URLSearchParams(src).toString();
});
})
).then((imgs) => {
this.imgs = imgs.filter(Boolean);
this.setSizeForImage?.();
app.graph.setDirtyCanvas(true);
});
app.nodeOutputs[id].images[0] = { ...node.widgets[0].value };
}
return app.nodeOutputs[id].images;
}
else {
return node._images;
}
}
});
}
}
});

641
custom_nodes/ComfyUI-Impact-Pack/js/impact-sam-editor.js Normal file
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@@ -0,0 +1,641 @@
import { app } from "../../scripts/app.js";
import { api } from "../../scripts/api.js";
import { ComfyDialog, $el } from "../../scripts/ui.js";
import { ComfyApp } from "../../scripts/app.js";
import { ClipspaceDialog } from "../../extensions/core/clipspace.js";
function addMenuHandler(nodeType, cb) {
const getOpts = nodeType.prototype.getExtraMenuOptions;
nodeType.prototype.getExtraMenuOptions = function () {
const r = getOpts.apply(this, arguments);
cb.apply(this, arguments);
return r;
};
}
// Helper function to convert a data URL to a Blob object
function dataURLToBlob(dataURL) {
const parts = dataURL.split(';base64,');
const contentType = parts[0].split(':')[1];
const byteString = atob(parts[1]);
const arrayBuffer = new ArrayBuffer(byteString.length);
const uint8Array = new Uint8Array(arrayBuffer);
for (let i = 0; i < byteString.length; i++) {
uint8Array[i] = byteString.charCodeAt(i);
}
return new Blob([arrayBuffer], { type: contentType });
}
function loadedImageToBlob(image) {
const canvas = document.createElement('canvas');
canvas.width = image.width;
canvas.height = image.height;
const ctx = canvas.getContext('2d');
ctx.drawImage(image, 0, 0);
const dataURL = canvas.toDataURL('image/png', 1);
const blob = dataURLToBlob(dataURL);
return blob;
}
async function uploadMask(filepath, formData) {
await api.fetchApi('/upload/mask', {
method: 'POST',
body: formData
}).then(response => {}).catch(error => {
console.error('Error:', error);
});
ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']] = new Image();
ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src = `view?filename=${filepath.filename}&type=${filepath.type}`;
if(ComfyApp.clipspace.images)
ComfyApp.clipspace.images[ComfyApp.clipspace['selectedIndex']] = filepath;
ClipspaceDialog.invalidatePreview();
}
class ImpactSamEditorDialog extends ComfyDialog {
static instance = null;
static getInstance() {
if(!ImpactSamEditorDialog.instance) {
ImpactSamEditorDialog.instance = new ImpactSamEditorDialog();
}
return ImpactSamEditorDialog.instance;
}
constructor() {
super();
this.element = $el("div.comfy-modal", { parent: document.body },
[ $el("div.comfy-modal-content",
[...this.createButtons()]),
]);
}
createButtons() {
return [];
}
createButton(name, callback) {
var button = document.createElement("button");
button.innerText = name;
button.addEventListener("click", callback);
return button;
}
createLeftButton(name, callback) {
var button = this.createButton(name, callback);
button.style.cssFloat = "left";
button.style.marginRight = "4px";
return button;
}
createRightButton(name, callback) {
var button = this.createButton(name, callback);
button.style.cssFloat = "right";
button.style.marginLeft = "4px";
return button;
}
createLeftSlider(self, name, callback) {
const divElement = document.createElement('div');
divElement.id = "sam-confidence-slider";
divElement.style.cssFloat = "left";
divElement.style.fontFamily = "sans-serif";
divElement.style.marginRight = "4px";
divElement.style.color = "var(--input-text)";
divElement.style.backgroundColor = "var(--comfy-input-bg)";
divElement.style.borderRadius = "8px";
divElement.style.borderColor = "var(--border-color)";
divElement.style.borderStyle = "solid";
divElement.style.fontSize = "15px";
divElement.style.height = "21px";
divElement.style.padding = "1px 6px";
divElement.style.display = "flex";
divElement.style.position = "relative";
divElement.style.top = "2px";
self.confidence_slider_input = document.createElement('input');
self.confidence_slider_input.setAttribute('type', 'range');
self.confidence_slider_input.setAttribute('min', '0');
self.confidence_slider_input.setAttribute('max', '100');
self.confidence_slider_input.setAttribute('value', '70');
const labelElement = document.createElement("label");
labelElement.textContent = name;
divElement.appendChild(labelElement);
divElement.appendChild(self.confidence_slider_input);
self.confidence_slider_input.addEventListener("change", callback);
return divElement;
}
async detect_and_invalidate_mask_canvas(self) {
const mask_img = await self.detect(self);
const canvas = self.maskCtx.canvas;
const ctx = self.maskCtx;
ctx.clearRect(0, 0, canvas.width, canvas.height);
await new Promise((resolve, reject) => {
self.mask_image = new Image();
self.mask_image.onload = function() {
ctx.drawImage(self.mask_image, 0, 0, canvas.width, canvas.height);
resolve();
};
self.mask_image.onerror = reject;
self.mask_image.src = mask_img.src;
});
}
setlayout(imgCanvas, maskCanvas, pointsCanvas) {
const self = this;
// If it is specified as relative, using it only as a hidden placeholder for padding is recommended
// to prevent anomalies where it exceeds a certain size and goes outside of the window.
var placeholder = document.createElement("div");
placeholder.style.position = "relative";
placeholder.style.height = "50px";
var bottom_panel = document.createElement("div");
bottom_panel.style.position = "absolute";
bottom_panel.style.bottom = "0px";
bottom_panel.style.left = "20px";
bottom_panel.style.right = "20px";
bottom_panel.style.height = "50px";
var brush = document.createElement("div");
brush.id = "sam-brush";
brush.style.backgroundColor = "blue";
brush.style.outline = "2px solid pink";
brush.style.borderRadius = "50%";
brush.style.MozBorderRadius = "50%";
brush.style.WebkitBorderRadius = "50%";
brush.style.position = "absolute";
brush.style.zIndex = 100;
brush.style.pointerEvents = "none";
this.brush = brush;
this.element.appendChild(imgCanvas);
this.element.appendChild(maskCanvas);
this.element.appendChild(pointsCanvas);
this.element.appendChild(placeholder); // must below z-index than bottom_panel to avoid covering button
this.element.appendChild(bottom_panel);
document.body.appendChild(brush);
this.brush_size = 5;
var confidence_slider = this.createLeftSlider(self, "Confidence", (event) => {
self.confidence = event.target.value;
});
var clearButton = this.createLeftButton("Clear", () => {
self.maskCtx.clearRect(0, 0, self.maskCanvas.width, self.maskCanvas.height);
self.pointsCtx.clearRect(0, 0, self.pointsCanvas.width, self.pointsCanvas.height);
self.prompt_points = [];
self.invalidatePointsCanvas(self);
});
var detectButton = this.createLeftButton("Detect", () => self.detect_and_invalidate_mask_canvas(self));
var cancelButton = this.createRightButton("Cancel", () => {
document.removeEventListener("mouseup", ImpactSamEditorDialog.handleMouseUp);
document.removeEventListener("keydown", ImpactSamEditorDialog.handleKeyDown);
self.close();
});
self.saveButton = this.createRightButton("Save", () => {
document.removeEventListener("mouseup", ImpactSamEditorDialog.handleMouseUp);
document.removeEventListener("keydown", ImpactSamEditorDialog.handleKeyDown);
self.save(self);
});
var undoButton = this.createLeftButton("Undo", () => {
if(self.prompt_points.length > 0) {
self.prompt_points.pop();
self.pointsCtx.clearRect(0, 0, self.pointsCanvas.width, self.pointsCanvas.height);
self.invalidatePointsCanvas(self);
}
});
bottom_panel.appendChild(clearButton);
bottom_panel.appendChild(detectButton);
bottom_panel.appendChild(self.saveButton);
bottom_panel.appendChild(cancelButton);
bottom_panel.appendChild(confidence_slider);
bottom_panel.appendChild(undoButton);
imgCanvas.style.position = "relative";
imgCanvas.style.top = "200";
imgCanvas.style.left = "0";
maskCanvas.style.position = "absolute";
maskCanvas.style.opacity = 0.5;
pointsCanvas.style.position = "absolute";
}
show() {
this.mask_image = null;
self.prompt_points = [];
this.message_box = $el("p", ["Please wait a moment while the SAM model and the image are being loaded."]);
this.element.appendChild(this.message_box);
if(self.imgCtx) {
self.imgCtx.clearRect(0, 0, self.imageCanvas.width, self.imageCanvas.height);
}
const target_image_path = ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src;
this.load_sam(target_image_path);
if(!this.is_layout_created) {
// layout
const imgCanvas = document.createElement('canvas');
const maskCanvas = document.createElement('canvas');
const pointsCanvas = document.createElement('canvas');
imgCanvas.id = "imageCanvas";
maskCanvas.id = "samEditorMaskCanvas";
pointsCanvas.id = "pointsCanvas";
this.setlayout(imgCanvas, maskCanvas, pointsCanvas);
// prepare content
this.imgCanvas = imgCanvas;
this.maskCanvas = maskCanvas;
this.pointsCanvas = pointsCanvas;
this.maskCtx = maskCanvas.getContext('2d');
this.pointsCtx = pointsCanvas.getContext('2d');
this.is_layout_created = true;
// replacement of onClose hook since close is not real close
const self = this;
const observer = new MutationObserver(function(mutations) {
mutations.forEach(function(mutation) {
if (mutation.type === 'attributes' && mutation.attributeName === 'style') {
if(self.last_display_style && self.last_display_style != 'none' && self.element.style.display == 'none') {
ComfyApp.onClipspaceEditorClosed();
}
self.last_display_style = self.element.style.display;
}
});
});
const config = { attributes: true };
observer.observe(this.element, config);
}
this.setImages(target_image_path, this.imgCanvas, this.pointsCanvas);
if(ComfyApp.clipspace_return_node) {
this.saveButton.innerText = "Save to node";
}
else {
this.saveButton.innerText = "Save";
}
this.saveButton.disabled = true;
this.element.style.display = "block";
this.element.style.zIndex = 8888; // NOTE: alert dialog must be high priority.
}
updateBrushPreview(self, event) {
event.preventDefault();
const centerX = event.pageX;
const centerY = event.pageY;
const brush = self.brush;
brush.style.width = self.brush_size * 2 + "px";
brush.style.height = self.brush_size * 2 + "px";
brush.style.left = (centerX - self.brush_size) + "px";
brush.style.top = (centerY - self.brush_size) + "px";
}
setImages(target_image_path, imgCanvas, pointsCanvas) {
const imgCtx = imgCanvas.getContext('2d');
const maskCtx = this.maskCtx;
const maskCanvas = this.maskCanvas;
const self = this;
// image load
const orig_image = new Image();
window.addEventListener("resize", () => {
// repositioning
imgCanvas.width = window.innerWidth - 250;
imgCanvas.height = window.innerHeight - 200;
// redraw image
let drawWidth = orig_image.width;
let drawHeight = orig_image.height;
if (orig_image.width > imgCanvas.width) {
drawWidth = imgCanvas.width;
drawHeight = (drawWidth / orig_image.width) * orig_image.height;
}
if (drawHeight > imgCanvas.height) {
drawHeight = imgCanvas.height;
drawWidth = (drawHeight / orig_image.height) * orig_image.width;
}
imgCtx.drawImage(orig_image, 0, 0, drawWidth, drawHeight);
// update mask
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 = pointsCanvas.width;
maskCanvas.height = pointsCanvas.height;
maskCanvas.style.top = imgCanvas.offsetTop + "px";
maskCanvas.style.left = imgCanvas.offsetLeft + "px";
self.invalidateMaskCanvas(self);
self.invalidatePointsCanvas(self);
});
// original image load
orig_image.onload = () => self.onLoaded(self);
const rgb_url = new URL(target_image_path);
rgb_url.searchParams.delete('channel');
rgb_url.searchParams.set('channel', 'rgb');
orig_image.src = rgb_url;
self.image = orig_image;
}
onLoaded(self) {
if(self.message_box) {
self.element.removeChild(self.message_box);
self.message_box = null;
}
window.dispatchEvent(new Event('resize'));
self.setEventHandler(pointsCanvas);
self.saveButton.disabled = false;
}
setEventHandler(targetCanvas) {
targetCanvas.addEventListener("contextmenu", (event) => {
event.preventDefault();
});
const self = this;
targetCanvas.addEventListener('pointermove', (event) => this.updateBrushPreview(self,event));
targetCanvas.addEventListener('pointerdown', (event) => this.handlePointerDown(self,event));
targetCanvas.addEventListener('pointerover', (event) => { this.brush.style.display = "block"; });
targetCanvas.addEventListener('pointerleave', (event) => { this.brush.style.display = "none"; });
document.addEventListener('keydown', ImpactSamEditorDialog.handleKeyDown);
}
static handleKeyDown(event) {
const self = ImpactSamEditorDialog.instance;
if (event.key === '=') { // positive
brush.style.backgroundColor = "blue";
brush.style.outline = "2px solid pink";
self.is_positive_mode = true;
} else if (event.key === '-') { // negative
brush.style.backgroundColor = "red";
brush.style.outline = "2px solid skyblue";
self.is_positive_mode = false;
}
}
is_positive_mode = true;
prompt_points = [];
confidence = 70;
invalidatePointsCanvas(self) {
const ctx = self.pointsCtx;
for (const i in self.prompt_points) {
const [is_positive, x, y] = self.prompt_points[i];
const scaledX = x * ctx.canvas.width / self.image.width;
const scaledY = y * ctx.canvas.height / self.image.height;
if(is_positive)
ctx.fillStyle = "blue";
else
ctx.fillStyle = "red";
ctx.beginPath();
ctx.arc(scaledX, scaledY, 3, 0, 3 * Math.PI);
ctx.fill();
}
}
invalidateMaskCanvas(self) {
if(self.mask_image) {
self.maskCtx.clearRect(0, 0, self.maskCanvas.width, self.maskCanvas.height);
self.maskCtx.drawImage(self.mask_image, 0, 0, self.maskCanvas.width, self.maskCanvas.height);
}
}
async load_sam(url) {
const parsedUrl = new URL(url);
const searchParams = new URLSearchParams(parsedUrl.search);
const filename = searchParams.get("filename") || "";
const fileType = searchParams.get("type") || "";
const subfolder = searchParams.get("subfolder") || "";
const data = {
sam_model_name: "auto",
filename: filename,
type: fileType,
subfolder: subfolder
};
api.fetchApi('/sam/prepare', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(data)
});
}
async detect(self) {
const positive_points = [];
const negative_points = [];
for(const i in self.prompt_points) {
const [is_positive, x, y] = self.prompt_points[i];
const point = [x,y];
if(is_positive) {
positive_points.push(point);
}
else
negative_points.push(point);
}
const data = {
positive_points: positive_points,
negative_points: negative_points,
threshold: self.confidence/100
};
const response = await api.fetchApi('/sam/detect', {
method: 'POST',
headers: { 'Content-Type': 'image/png' },
body: JSON.stringify(data)
});
const blob = await response.blob();
const url = URL.createObjectURL(blob);
return new Promise((resolve, reject) => {
const image = new Image();
image.onload = () => resolve(image);
image.onerror = reject;
image.src = url;
});
}
handlePointerDown(self, event) {
if ([0, 2, 5].includes(event.button)) {
event.preventDefault();
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.clientWidth;
const originalY = y * self.image.height / self.pointsCanvas.clientHeight;
var point = null;
if (event.button == 0) {
// positive
point = [true, originalX, originalY];
} else {
// negative
point = [false, originalX, originalY];
}
self.prompt_points.push(point);
self.invalidatePointsCanvas(self);
}
}
async save(self) {
if(!self.mask_image) {
this.close();
return;
}
const save_canvas = document.createElement('canvas');
const save_ctx = save_canvas.getContext('2d', {willReadFrequently:true});
save_canvas.width = self.mask_image.width;
save_canvas.height = self.mask_image.height;
save_ctx.drawImage(self.mask_image, 0, 0, save_canvas.width, save_canvas.height);
const save_data = save_ctx.getImageData(0, 0, save_canvas.width, save_canvas.height);
// refine mask image
for (let i = 0; i < save_data.data.length; i += 4) {
if(save_data.data[i]) {
save_data.data[i+3] = 0;
}
else {
save_data.data[i+3] = 255;
}
save_data.data[i] = 0;
save_data.data[i+1] = 0;
save_data.data[i+2] = 0;
}
save_ctx.globalCompositeOperation = 'source-over';
save_ctx.putImageData(save_data, 0, 0);
const formData = new FormData();
const filename = "clipspace-mask-" + performance.now() + ".png";
const item =
{
"filename": filename,
"subfolder": "",
"type": "temp",
};
if(ComfyApp.clipspace.images)
ComfyApp.clipspace.images[0] = item;
if(ComfyApp.clipspace.widgets) {
const index = ComfyApp.clipspace.widgets.findIndex(obj => obj.name === 'image');
if(index >= 0)
ComfyApp.clipspace.widgets[index].value = `${filename} [temp]`;
}
const dataURL = save_canvas.toDataURL();
const blob = dataURLToBlob(dataURL);
let original_url = new URL(this.image.src);
const original_ref = { filename: original_url.searchParams.get('filename') };
let original_subfolder = original_url.searchParams.get("subfolder");
if(original_subfolder)
original_ref.subfolder = original_subfolder;
let original_type = original_url.searchParams.get("type");
if(original_type)
original_ref.type = original_type;
formData.append('image', blob, filename);
formData.append('original_ref', JSON.stringify(original_ref));
formData.append('type', "temp");
await uploadMask(item, formData);
ComfyApp.onClipspaceEditorSave();
this.close();
}
}
app.registerExtension({
name: "Comfy.Impact.SAMEditor",
init(app) {
const callback =
function () {
let dlg = ImpactSamEditorDialog.getInstance();
dlg.show();
};
const context_predicate = () => ComfyApp.clipspace && ComfyApp.clipspace.imgs && ComfyApp.clipspace.imgs.length > 0
ClipspaceDialog.registerButton("Impact SAM Detector", context_predicate, callback);
},
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (Array.isArray(nodeData.output) && (nodeData.output.includes("MASK") || nodeData.output.includes("IMAGE"))) {
addMenuHandler(nodeType, function (_, options) {
options.unshift({
content: "Open in SAM Detector",
callback: () => {
ComfyApp.copyToClipspace(this);
ComfyApp.clipspace_return_node = this;
let dlg = ImpactSamEditorDialog.getInstance();
dlg.show();
},
});
});
}
}
});

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import { ComfyApp, app } from "../../scripts/app.js";
import { ComfyDialog, $el } from "../../scripts/ui.js";
import { api } from "../../scripts/api.js";
async function open_picker(node) {
const resp = await api.fetchApi(`/impact/segs/picker/count?id=${node.id}`);
const body = await resp.text();
let cnt = parseInt(body);
var existingPicker = document.getElementById('impact-picker');
if (existingPicker) {
existingPicker.parentNode.removeChild(existingPicker);
}
var gallery = document.createElement('div');
gallery.id = 'impact-picker';
gallery.style.position = "absolute";
gallery.style.height = "80%";
gallery.style.width = "80%";
gallery.style.top = "10%";
gallery.style.left = "10%";
gallery.style.display = 'flex';
gallery.style.flexWrap = 'wrap';
gallery.style.maxHeight = '600px';
gallery.style.overflow = 'auto';
gallery.style.backgroundColor = 'rgba(0,0,0,0.3)';
gallery.style.padding = '20px';
gallery.draggable = false;
gallery.style.zIndex = 5000;
var doneButton = document.createElement('button');
doneButton.textContent = 'Done';
doneButton.style.padding = '10px 10px';
doneButton.style.border = 'none';
doneButton.style.borderRadius = '5px';
doneButton.style.fontFamily = 'Arial, sans-serif';
doneButton.style.fontSize = '16px';
doneButton.style.fontWeight = 'bold';
doneButton.style.color = '#fff';
doneButton.style.background = 'linear-gradient(to bottom, #0070B8, #003D66)';
doneButton.style.boxShadow = '0 2px 4px rgba(0, 0, 0, 0.4)';
doneButton.style.margin = "20px";
doneButton.style.height = "40px";
var cancelButton = document.createElement('button');
cancelButton.textContent = 'Cancel';
cancelButton.style.padding = '10px 10px';
cancelButton.style.border = 'none';
cancelButton.style.borderRadius = '5px';
cancelButton.style.fontFamily = 'Arial, sans-serif';
cancelButton.style.fontSize = '16px';
cancelButton.style.fontWeight = 'bold';
cancelButton.style.color = '#fff';
cancelButton.style.background = 'linear-gradient(to bottom, #ff70B8, #ff3D66)';
cancelButton.style.boxShadow = '0 2px 4px rgba(0, 0, 0, 0.4)';
cancelButton.style.margin = "20px";
cancelButton.style.height = "40px";
const w = node.widgets.find((w) => w.name == 'picks');
let prev_selected = w.value.split(',').map(function(item) {
return parseInt(item, 10);
});
let images = [];
doneButton.onclick = () => {
var result = '';
for(let i in images) {
if(images[i].isSelected) {
if(result != '')
result += ', ';
result += (parseInt(i)+1);
}
}
w.value = result;
gallery.parentNode.removeChild(gallery);
}
cancelButton.onclick = () => {
gallery.parentNode.removeChild(gallery);
}
var panel = document.createElement('div');
panel.style.clear = 'both';
panel.style.width = '100%';
panel.style.height = '40px';
panel.style.justifyContent = 'center';
panel.style.alignItems = 'center';
panel.style.display = 'flex';
panel.appendChild(doneButton);
panel.appendChild(cancelButton);
gallery.appendChild(panel);
var hint = document.createElement('label');
hint.style.position = 'absolute';
hint.innerHTML = 'Click: Toggle Selection<BR>Ctrl-click: Single Selection';
gallery.appendChild(hint);
let max_size = 300;
for(let i=0; i<cnt; i++) {
let image = new Image();
image.src = `/impact/segs/picker/view?id=${node.id}&idx=${i}`;
image.style.margin = '10px';
image.draggable = false;
images.push(image);
image.isSelected = prev_selected.includes(i + 1);
if(image.isSelected) {
image.style.border = '2px solid #006699';
}
image.onload = function() {
var ratio = 1.0;
if(image.naturalWidth > image.naturalHeight) {
ratio = max_size/image.naturalWidth;
}
else {
ratio = max_size/image.naturalHeight;
}
let width = image.naturalWidth * ratio;
let height = image.naturalHeight * ratio;
if(width < height) {
this.style.marginLeft = (200-width)/2+"px";
}
else{
this.style.marginTop = (200-height)/2+"px";
}
this.style.width = width+"px";
this.style.height = height+"px";
this.style.objectFit = 'cover';
}
image.addEventListener('click', function(event) {
if(event.ctrlKey) {
for(let i in images) {
if(images[i].isSelected) {
images[i].style.border = 'none';
images[i].isSelected = false;
}
}
image.style.border = '2px solid #006699';
image.isSelected = true;
return;
}
if(image.isSelected) {
image.style.border = 'none';
image.isSelected = false;
}
else {
image.style.border = '2px solid #006699';
image.isSelected = true;
}
});
gallery.appendChild(image);
}
document.body.appendChild(gallery);
}
app.registerExtension({
name: "Comfy.Impack.Picker",
nodeCreated(node, app) {
if(node.comfyClass == "ImpactSEGSPicker") {
node.addWidget("button", "pick", "image", () => {
open_picker(node);
});
}
}
});

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import { app } from "../../scripts/app.js";
import { readLinkedNumber, getDrawColor, computeCanvasSize } from "./common.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, 220, 240);
}
const visible = true;
const t = ctx.getTransform();
const margin = 12;
const border = 2;
const widgetHeight = node.canvasHeight;
// Keep preview in sync when inputs are driven by links.
syncLinkedInputsToPropertiesAdvanced(node);
const width = Math.max(1, Math.round(node.properties["width"]));
const height = Math.max(1, 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;
}
// Adjust X and Y coordinates
const barHeight = 8;
let widgetYBar = widgetY + backgroundHeight + margin;
// Draw the border around the progress bar
ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR;
ctx.fillRect(
widgetX - border,
widgetYBar - border,
backgroundWidth + border * 2,
barHeight + border * 2
);
// Draw the main bar area (background)
ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR;
ctx.fillRect(
widgetX,
widgetYBar,
backgroundWidth,
barHeight
);
// Draw progress bar grid
ctx.beginPath();
ctx.lineWidth = 1;
ctx.strokeStyle = "#66666650";
// Calculate the number of grid lines based on the bar size
const numLines = Math.floor(backgroundWidth / 64);
// Draw grid lines
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 (based on 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;
widget.computeLayoutSize = function (node) {
return {
minHeight: 200,
maxHeight: 300
};
};
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, 220, 240);
};
return {minWidth: 200, minHeight: 200, widget};
}
app.registerExtension({
name: "drltdata.MaskRectAreaAdvanced",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (nodeData.name !== "MaskRectAreaAdvanced") {
return;
}
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;
// If the node already provides widgets from Python/ComfyUI, do NOT recreate them
const hasExisting = Array.isArray(this.widgets) && this.widgets.some(w => w && w.name === "x");
// Helper: attach callbacks to existing widgets to keep node.properties in sync (canvas preview).
const hookWidget = (node, widgetName, propName, opts) => {
if (!Array.isArray(node.widgets)) {
return;
}
const w = node.widgets.find(ww => ww && ww.name === widgetName);
if (!w) {
return;
}
const min = (opts && typeof opts.min === "number") ? opts.min : undefined;
const max = (opts && typeof opts.max === "number") ? opts.max : undefined;
const step = (opts && typeof opts.step === "number") ? opts.step : undefined;
if (node.properties && Object.prototype.hasOwnProperty.call(node.properties, propName)) {
w.value = node.properties[propName];
} else {
node.properties[propName] = w.value;
}
const prevCb = w.callback;
w.callback = function (v, ...args) {
let val = v;
if (typeof val === "number") {
if (typeof step === "number" && step > 0) {
const s = step / 10;
val = Math.round(val / s) * s;
} else {
val = Math.round(val);
}
if (typeof min === "number") {
val = Math.max(min, val);
}
if (typeof max === "number") {
val = Math.min(max, val);
}
}
this.value = val;
node.properties[propName] = val;
if (prevCb) {
return prevCb.call(this, val, ...args);
}
};
};
if (hasExisting) {
hookWidget(this, "x", "x", {"step": 10});
hookWidget(this, "y", "y", {"step": 10});
hookWidget(this, "width", "w", {"step": 10});
hookWidget(this, "height", "h", {"step": 10});
hookWidget(this, "image_width", "width", {"step": 10});
hookWidget(this, "image_height", "height", {"step": 10});
hookWidget(this, "blur_radius", "blur_radius", {"min": 0, "max": 255, "step": 10});
} else {
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 syncLinkedInputsToPropertiesAdvanced(node) {
let changed = false;
const vx = readLinkedNumber(node, "x");
if (vx != null) {
const nv = Math.max(0, Math.round(vx));
if (node.properties["x"] !== nv) {
node.properties["x"] = nv;
changed = true;
}
}
const vy = readLinkedNumber(node, "y");
if (vy != null) {
const nv = Math.max(0, Math.round(vy));
if (node.properties["y"] !== nv) {
node.properties["y"] = nv;
changed = true;
}
}
// Input "width" is the rectangle width in px -> property "w"
const vw = readLinkedNumber(node, "width");
if (vw != null) {
const nv = Math.max(0, Math.round(vw));
if (node.properties["w"] !== nv) {
node.properties["w"] = nv;
changed = true;
}
}
// Input "height" is the rectangle height in px -> property "h"
const vh = readLinkedNumber(node, "height");
if (vh != null) {
const nv = Math.max(0, Math.round(vh));
if (node.properties["h"] !== nv) {
node.properties["h"] = nv;
changed = true;
}
}
// Image size (must be >=1 to avoid division by zero in getDrawArea)
const viw = readLinkedNumber(node, "image_width");
if (viw != null) {
const nv = Math.max(1, Math.round(viw));
if (node.properties["width"] !== nv) {
node.properties["width"] = nv;
changed = true;
}
}
const vih = readLinkedNumber(node, "image_height");
if (vih != null) {
const nv = Math.max(1, Math.round(vih));
if (node.properties["height"] !== nv) {
node.properties["height"] = nv;
changed = true;
}
}
const vbr = readLinkedNumber(node, "blur_radius");
if (vbr != null) {
const nv = Math.max(0, Math.min(255, Math.round(vbr)));
if (node.properties["blur_radius"] !== nv) {
node.properties["blur_radius"] = nv;
changed = true;
}
}
return changed;
}

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custom_nodes/ComfyUI-Impact-Pack/js/mask-rect-area.js Normal file
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import { app } from "../../scripts/app.js";
import { readLinkedNumber, getDrawColor, computeCanvasSize } from "./common.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, 200, 200);
}
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);
// Keep preview in sync when inputs are driven by links.
syncLinkedInputsToProperties(node);
// 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;
}
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;
widget.computeLayoutSize = function (node) {
return {
minHeight: 200,
maxHeight: 300
};
};
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, 200, 200);
};
return {minWidth: 200, minHeight: 200, widget};
}
app.registerExtension({
name: 'drltdata.MaskRectArea',
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (nodeData.name !== "MaskRectArea") {
return;
}
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;
// If Python/ComfyUI already created typed widgets, do not recreate them (avoid duplicates).
const hasExisting = Array.isArray(this.widgets) && this.widgets.some(w => w && w.name === "x");
// Hook existing widgets to keep node.properties in sync (canvas uses properties).
const hookWidget = (node, widgetName, propName, opts) => {
if (!Array.isArray(node.widgets)) {
return;
}
const w = node.widgets.find(ww => ww && ww.name === widgetName);
if (!w) {
return;
}
const min = (opts && typeof opts.min === "number") ? opts.min : undefined;
const max = (opts && typeof opts.max === "number") ? opts.max : undefined;
if (node.properties && Object.prototype.hasOwnProperty.call(node.properties, propName)) {
w.value = node.properties[propName];
} else {
node.properties[propName] = w.value;
}
const prevCb = w.callback;
w.callback = function (v, ...args) {
let val = v;
if (typeof val === "number") {
val = Math.round(val);
if (typeof min === "number") {
val = Math.max(min, val);
}
if (typeof max === "number") {
val = Math.min(max, val);
}
}
this.value = val;
node.properties[propName] = val;
if (prevCb) {
return prevCb.call(this, val, ...args);
}
};
};
if (hasExisting) {
// Note: "width"/"height" widgets map to "w"/"h" properties (percent-based).
hookWidget(this, "x", "x", {"min": 0, "max": 100});
hookWidget(this, "y", "y", {"min": 0, "max": 100});
hookWidget(this, "width", "w", {"min": 0, "max": 100});
hookWidget(this, "height", "h", {"min": 0, "max": 100});
hookWidget(this, "blur_radius", "blur_radius", {"min": 0, "max": 255});
} else {
CUSTOM_INT(this, "x", 0, function (v, _, node) {
this.value = Math.max(0, Math.min(100, Math.round(v)));
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});
// If Python widgets exist, they will be used instead; this is back-compat only.
}
showPreviewCanvas(this, app);
// Sync linked input values -> node.properties so the preview updates when driven by connections.
const prevOnExecute = this.onExecute;
this.onExecute = function () {
const rr = prevOnExecute ? prevOnExecute.apply(this, arguments) : undefined;
const readLinkedInt = (inputName) => {
if (!Array.isArray(this.inputs)) {
return null;
}
const inp = this.inputs.find(i => i && i.name === inputName);
if (!inp || !inp.link) {
return null;
}
try {
const v = this.getInputData(inputName);
return (typeof v === "number") ? v : null;
} catch (e) {
return null;
}
};
let changed = false;
const vx = readLinkedInt("x");
if (vx != null) {
const nv = Math.max(0, Math.min(100, Math.round(vx)));
if (this.properties["x"] !== nv) {
this.properties["x"] = nv;
changed = true;
}
}
const vy = readLinkedInt("y");
if (vy != null) {
const nv = Math.max(0, Math.min(100, Math.round(vy)));
if (this.properties["y"] !== nv) {
this.properties["y"] = nv;
changed = true;
}
}
const vw = readLinkedInt("width");
if (vw != null) {
const nv = Math.max(0, Math.min(100, Math.round(vw)));
if (this.properties["w"] !== nv) {
this.properties["w"] = nv;
changed = true;
}
}
const vh = readLinkedInt("height");
if (vh != null) {
const nv = Math.max(0, Math.min(100, Math.round(vh)));
if (this.properties["h"] !== nv) {
this.properties["h"] = nv;
changed = true;
}
}
const vbr = readLinkedInt("blur_radius");
if (vbr != null) {
const nv = Math.max(0, Math.min(255, Math.round(vbr)));
if (this.properties["blur_radius"] !== nv) {
this.properties["blur_radius"] = nv;
changed = true;
}
}
if (changed) {
this.setDirtyCanvas(true, true);
if (this.graph) {
this.graph.setDirtyCanvas(true, true);
}
}
return rr;
};
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 syncLinkedInputsToProperties(node) {
let changed = false;
const vx = readLinkedNumber(node, "x");
if (vx != null) {
const nv = Math.max(0, Math.min(100, Math.round(vx)));
if (node.properties["x"] !== nv) {
node.properties["x"] = nv;
changed = true;
}
}
const vy = readLinkedNumber(node, "y");
if (vy != null) {
const nv = Math.max(0, Math.min(100, Math.round(vy)));
if (node.properties["y"] !== nv) {
node.properties["y"] = nv;
changed = true;
}
}
const vw = readLinkedNumber(node, "width");
if (vw != null) {
const nv = Math.max(0, Math.min(100, Math.round(vw)));
if (node.properties["w"] !== nv) {
node.properties["w"] = nv;
changed = true;
}
}
const vh = readLinkedNumber(node, "height");
if (vh != null) {
const nv = Math.max(0, Math.min(100, Math.round(vh)));
if (node.properties["h"] !== nv) {
node.properties["h"] = nv;
changed = true;
}
}
const vbr = readLinkedNumber(node, "blur_radius");
if (vbr != null) {
const nv = Math.max(0, Math.min(255, Math.round(vbr)));
if (node.properties["blur_radius"] !== nv) {
node.properties["blur_radius"] = nv;
changed = true;
}
}
return changed;
}

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custom_nodes/ComfyUI-Impact-Pack/modules/impact/additional_dependencies.py Normal file
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import sys
import subprocess
def ensure_onnx_package():
try:
import onnxruntime # noqa: F401
except Exception:
if "python_embeded" in sys.executable or "python_embedded" in sys.executable:
subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', 'onnxruntime'])
else:
subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', 'onnxruntime'])

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custom_nodes/ComfyUI-Impact-Pack/modules/impact/animatediff_nodes.py Normal file
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from nodes import MAX_RESOLUTION
import impact.core as core
from impact.core import SEG
from impact.segs_nodes import SEGSPaste
import comfy
from impact import utils
import torch
import nodes
import logging
try:
from comfy_extras import nodes_differential_diffusion
except Exception:
logging.warning("\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": 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": (core.get_schedulers(),),
"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
"basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
},
"optional": {
"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 = ("SEGS", "IMAGE")
RETURN_NAMES = ("segs", "cnet_images")
OUTPUT_IS_LIST = (False, True)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detailer"
DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is applied specifically to SEGS rather than the entire image. To apply it to the entire image, use the 'SEGS Paste' node.\nAs a specialized detailer node for improving video details, such as in AnimateDiff, this node can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames."
@staticmethod
def do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, noise_mask_feather=0, scheduler_func_opt=None):
model, clip, vae, positive, negative = basic_pipe
if refiner_basic_pipe_opt is None:
refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None
else:
refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt
segs = core.segs_scale_match(segs, image_frames.shape)
new_segs = []
cnet_image_list = []
if not (isinstance(model, str) and model == "DUMMY") and noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
model = nodes_differential_diffusion.DifferentialDiffusion().execute(model)[0]
for seg in segs[1]:
cropped_image_frames = None
for image in image_frames:
image = image.unsqueeze(0)
cropped_image = seg.cropped_image if seg.cropped_image is not None else utils.crop_tensor4(image, seg.crop_region)
cropped_image = utils.to_tensor(cropped_image)
if cropped_image_frames is None:
cropped_image_frames = cropped_image
else:
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
]
if not (isinstance(model, str) and model == "DUMMY"):
enhanced_image_tensor, cnet_images = core.enhance_detail_for_animatediff(cropped_image_frames, model, clip, vae, guide_size, guide_size_for, max_size,
seg.bbox, seed, steps, cfg, sampler_name, scheduler,
cropped_positive, cropped_negative, denoise, seg.cropped_mask,
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
refiner_clip=refiner_clip, refiner_positive=refiner_positive,
refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper,
noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt)
else:
enhanced_image_tensor = cropped_image_frames
cnet_images = None
if cnet_images is not None:
cnet_image_list.extend(cnet_images)
if enhanced_image_tensor is None:
new_cropped_image = cropped_image_frames
else:
new_cropped_image = enhanced_image_tensor.cpu().numpy()
new_seg = SEG(new_cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None)
new_segs.append(new_seg)
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, 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,
noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
if len(cnet_images) == 0:
cnet_images = [utils.empty_pil_tensor()]
return (segs, cnet_images)
class DetailerForEachPipeForAnimateDiff:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"image_frames": ("IMAGE", ),
"segs": ("SEGS", ),
"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": (core.get_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", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
},
"optional": {
"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")
RETURN_NAMES = ("image", "segs", "basic_pipe", "cnet_images")
OUTPUT_IS_LIST = (False, False, False, True)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detailer"
DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is a specialized detailer node for enhancing video details, such as in AnimateDiff. It can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames."
@staticmethod
def doit(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
denoise, feather, basic_pipe, refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None,
noise_mask_feather=0, scheduler_func_opt=None):
enhanced_segs = []
cnet_image_list = []
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, noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
image_frames = SEGSPaste.doit(image_frames, enhanced_seg, feather, alpha=255)[0]
if cnet_images is not None:
cnet_image_list.extend(cnet_images)
if detailer_hook is not None:
image_frames = detailer_hook.post_paste(image_frames)
enhanced_segs += enhanced_seg[1]
new_segs = segs[0], enhanced_segs
return image_frames, new_segs, basic_pipe, cnet_image_list

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custom_nodes/ComfyUI-Impact-Pack/modules/impact/bridge_nodes.py Normal file
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import os
from PIL import ImageOps
import logging
import folder_paths
import torch
import nodes
from PIL import Image
import numpy as np
from impact import utils
# 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):
return {"required": {
"images": ("IMAGE",),
"image": ("STRING", {"default": ""}),
},
"optional": {
"block": ("BOOLEAN", {"default": False, "label_on": "if_empty_mask", "label_off": "never", "tooltip": "is_empty_mask: If the mask is empty, the execution is stopped.\nnever: The execution is never stopped."}),
"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", )
FUNCTION = "doit"
OUTPUT_NODE = True
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()
self.type = "temp"
self.prev_hash = None
@staticmethod
def load_image(pb_id):
is_fail = False
if pb_id not in core.preview_bridge_image_id_map:
is_fail = True
if not is_fail:
image_path, ui_item = core.preview_bridge_image_id_map[pb_id]
if not os.path.isfile(image_path):
is_fail = True
if not is_fail:
i = Image.open(image_path)
i = ImageOps.exif_transpose(i)
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
else:
image = utils.empty_pil_tensor()
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
ui_item = {
"filename": 'empty.png',
"subfolder": '',
"type": 'temp'
}
return image, mask.unsqueeze(0), ui_item
@staticmethod
def register_clipspace_image(clipspace_path, node_id):
"""Register a clipspace image file in the preview bridge system.
This handles the case where ComfyUI's mask editor creates clipspace files
that need to be integrated with the preview bridge system.
"""
# Remove [input] suffix if present
clean_path = clipspace_path.replace(" [input]", "").replace("[input]", "")
# Try to find the actual clipspace file
input_dir = folder_paths.get_input_directory()
potential_paths = [
clean_path,
os.path.join(input_dir, clean_path),
os.path.join(input_dir, "clipspace", os.path.basename(clean_path)),
os.path.abspath(clean_path),
]
actual_file = None
for path in potential_paths:
if os.path.isfile(path):
actual_file = path
break
if not actual_file:
return False
# Create ui_item for the clipspace file
ui_item = {
'filename': os.path.basename(actual_file),
'subfolder': 'clipspace',
'type': 'input'
}
# Register it using the preview bridge system
core.set_previewbridge_image(node_id, actual_file, ui_item)
# Also register under the original clipspace path for compatibility
core.preview_bridge_image_id_map[clipspace_path] = (actual_file, ui_item)
return True
def doit(self, images, image, unique_id, block=False, restore_mask="never", prompt=None, extra_pnginfo=None):
need_refresh = False
images_changed = False
# Check if images have changed (this determines if we start fresh)
if unique_id not in core.preview_bridge_cache:
need_refresh = True
images_changed = True
elif core.preview_bridge_cache[unique_id][0] is not images:
need_refresh = True
images_changed = True
# If images changed, clear the mask cache to ensure fresh start behavior
# This restores the original behavior where new images start with empty masks
# unless restore_mask is set to "always" or "if_same_size"
if images_changed and restore_mask not in ["always", "if_same_size"] and unique_id in core.preview_bridge_last_mask_cache:
del core.preview_bridge_last_mask_cache[unique_id]
# Handle clipspace files that aren't registered in the preview bridge system
# This only applies when images haven't changed (same image, new mask scenario)
if not need_refresh and image not in core.preview_bridge_image_id_map:
# Check if this is a clipspace file that needs to be registered
is_clipspace = image and ("clipspace" in image.lower() or "[input]" in image)
if is_clipspace:
if not PreviewBridge.register_clipspace_image(image, unique_id):
need_refresh = True
else:
need_refresh = True
if not need_refresh:
pixels, mask, path_item = PreviewBridge.load_image(image)
image = [path_item]
else:
# For new images (images_changed=True), we want to start fresh regardless of restore_mask
# For same image with refresh needed, respect the restore_mask setting
# Exception: when restore_mask is "always", restore even with new images
# Exception: when restore_mask is "if_same_size", allow restoration to check size compatibility
if restore_mask != "never" and (not images_changed or restore_mask in ["always", "if_same_size"]):
mask = core.preview_bridge_last_mask_cache.get(unique_id)
if mask is None:
mask = None
elif restore_mask == "if_same_size" and mask.shape[1:] != images.shape[1:3]:
# For if_same_size, clear mask if dimensions don't match
mask = None
# For "always", keep the mask regardless of size
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 = utils.tensor_convert_rgba(images)
resized_mask = utils.resize_mask(mask, (images.shape[1], images.shape[2])).unsqueeze(3)
resized_mask = 1 - resized_mask
utils.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
path = os.path.join(folder_paths.get_temp_directory(), 'PreviewBridge', image2[0]['filename'])
core.set_previewbridge_image(unique_id, path, image2[0])
core.preview_bridge_image_id_map[image] = (path, image2[0])
core.preview_bridge_image_name_map[unique_id, path] = (image, image2[0])
core.preview_bridge_cache[unique_id] = (images, image2)
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:
logging.warning("[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": result,
}
def decode_latent(latent, preview_method, vae_opt=None):
if vae_opt is not None:
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"
elif preview_method == 'TAESDXL':
decoder_name = "taesdxl"
elif preview_method == 'TAESD3':
decoder_name = "taesd3"
elif preview_method == 'TAEF1':
decoder_name = "taef1"
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:
logging.warning(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)
samples = latent_format.process_in(latent['samples'])
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=utils.LANCZOS)
return utils.to_tensor(resized_image).unsqueeze(0)
class PreviewBridgeLatent:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"latent": ("LATENT",),
"image": ("STRING", {"default": ""}),
"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", ),
"block": ("BOOLEAN", {"default": False, "label_on": "if_empty_mask", "label_off": "never", "tooltip": "is_empty_mask: If the mask is empty, the execution is stopped.\nnever: The execution is never stopped. Instead, it returns a white mask."}),
"restore_mask": (["never", "always", "if_same_size"], {"tooltip": "if_same_size: If the changed input latent is the same size as the previous latent, restore using the last saved mask\nalways: Whenever the input latent changes, always restore using the last saved mask\nnever: Do not restore the mask.\n`restore_mask` has higher priority than `block`\nIf the input latent already has a mask, do not restore mask."}),
},
"hidden": {"unique_id": "UNIQUE_ID", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ("LATENT", "MASK", )
FUNCTION = "doit"
OUTPUT_NODE = True
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()
self.type = "temp"
self.prev_hash = None
self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
@staticmethod
def load_image(pb_id):
is_fail = False
if pb_id not in core.preview_bridge_image_id_map:
is_fail = True
if not is_fail:
image_path, ui_item = core.preview_bridge_image_id_map[pb_id]
if not os.path.isfile(image_path):
is_fail = True
if not is_fail:
i = Image.open(image_path)
i = ImageOps.exif_transpose(i)
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = None
else:
image = utils.empty_pil_tensor()
mask = None
ui_item = {
"filename": 'empty.png',
"subfolder": '',
"type": 'temp'
}
return image, mask, ui_item
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:
logging.warning("[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
latent_changed = False
# Check if latent has changed
if unique_id not in core.preview_bridge_cache:
need_refresh = True
latent_changed = True
elif (core.preview_bridge_cache[unique_id][0] is not latent
or (vae_opt is None and core.preview_bridge_cache[unique_id][2] is not None)
or (vae_opt is None and core.preview_bridge_cache[unique_id][1] != preview_method)
or (vae_opt is not None and core.preview_bridge_cache[unique_id][2] is not vae_opt)):
need_refresh = True
latent_changed = True
# If latent changed, clear the mask cache to ensure fresh start behavior
# unless restore_mask is set to "always" or "if_same_size"
if latent_changed and restore_mask not in ["always", "if_same_size"] and unique_id in core.preview_bridge_last_mask_cache:
del core.preview_bridge_last_mask_cache[unique_id]
# Handle clipspace files that aren't registered in the preview bridge system
# This only applies when latent hasn't changed (same latent, new mask scenario)
if not need_refresh and image not in core.preview_bridge_image_id_map:
is_clipspace = image and ("clipspace" in image.lower() or "[input]" in image)
if is_clipspace:
if not PreviewBridge.register_clipspace_image(image, unique_id):
need_refresh = True
else:
need_refresh = True
if not need_refresh:
pixels, mask, path_item = PreviewBridge.load_image(image)
if mask is None:
mask = torch.ones(latent['samples'].shape[2:], dtype=torch.float32, device="cpu").unsqueeze(0)
if 'noise_mask' in latent:
res_latent = latent.copy()
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'].squeeze(0) # 4D mask -> 3D mask
decoded_pil = utils.to_pil(decoded_image)
inverted_mask = 1 - mask # invert
resized_mask = utils.resize_mask(inverted_mask, (decoded_image.shape[1], decoded_image.shape[2]))
result_pil = utils.apply_mask_alpha_to_pil(decoded_pil, resized_mask)
full_output_folder, filename, counter, _, _ = folder_paths.get_save_image_path("PreviewBridge/PBL-"+self.prefix_append, folder_paths.get_temp_directory(), result_pil.size[0], result_pil.size[1])
file = f"{filename}_{counter}.png"
result_pil.save(os.path.join(full_output_folder, file), compress_level=4)
res_image = [{
'filename': file,
'subfolder': 'PreviewBridge',
'type': 'temp',
}]
is_empty_mask = False
else:
# For new latents (latent_changed=True), start fresh regardless of restore_mask
# For same latent with refresh needed, respect the restore_mask setting
# Exception: when restore_mask is "always", restore even with new latents
# Exception: when restore_mask is "if_same_size", allow restoration to check size compatibility
if restore_mask != "never" and (not latent_changed or restore_mask in ["always", "if_same_size"]):
mask = core.preview_bridge_last_mask_cache.get(unique_id)
if mask is None:
mask = None
elif restore_mask == "if_same_size" and mask.shape[1:] != decoded_image.shape[1:3]:
# For if_same_size, clear mask if dimensions don't match
mask = None
# For "always", keep the mask regardless of size
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 = utils.tensor_convert_rgba(decoded_image)
resized_mask = utils.resize_mask(mask, (decoded_image.shape[1], decoded_image.shape[2])).unsqueeze(3)
resized_mask = 1 - resized_mask
utils.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])
core.preview_bridge_image_name_map[unique_id, path] = (image, res_image[0])
core.preview_bridge_cache[unique_id] = (latent, preview_method, vae_opt, res_image)
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:
logging.warning("[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": result,
}

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import configparser
import logging
import os
version_code = [8, 28, 2]
version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
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")
def write_config():
config = configparser.ConfigParser()
config['default'] = {
'sam_editor_cpu': str(get_config()['sam_editor_cpu']),
'sam_editor_model': get_config()['sam_editor_model'],
'custom_wildcards': get_config()['custom_wildcards'],
'disable_gpu_opencv': get_config()['disable_gpu_opencv'],
'wildcard_cache_limit_mb': str(get_config()['wildcard_cache_limit_mb']),
}
with open(config_path, 'w') as configfile:
config.write(configfile)
def read_config():
try:
config = configparser.ConfigParser()
config.read(config_path)
default_conf = config['default']
# Strip quotes from custom_wildcards path if present
custom_wildcards_path = default_conf.get('custom_wildcards', '').strip('\'"')
if not os.path.exists(custom_wildcards_path):
logging.warning(f"[Impact Pack] custom_wildcards path not found: {custom_wildcards_path}. Using default path.")
custom_wildcards_path = os.path.join(my_path, "..", "..", "custom_wildcards")
default_conf['custom_wildcards'] = custom_wildcards_path
# Parse wildcard_cache_limit_mb with default value of 50MB
cache_limit_mb = 50
if 'wildcard_cache_limit_mb' in default_conf:
try:
cache_limit_mb = float(default_conf['wildcard_cache_limit_mb'])
except ValueError:
logging.warning(f"[Impact Pack] Invalid wildcard_cache_limit_mb value: {default_conf['wildcard_cache_limit_mb']}. Using default: 50")
cache_limit_mb = 50
return {
'sam_editor_cpu': default_conf['sam_editor_cpu'].lower() == 'true' if 'sam_editor_cpu' in default_conf else False,
'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,
'wildcard_cache_limit_mb': cache_limit_mb
}
except Exception:
return {
'sam_editor_cpu': False,
'sam_editor_model': 'sam_vit_b_01ec64.pth',
'custom_wildcards': os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "custom_wildcards")),
'disable_gpu_opencv': True,
'wildcard_cache_limit_mb': 50
}
cached_config = None
def get_config():
global cached_config
if cached_config is None:
cached_config = read_config()
return cached_config

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detection_labels = [
'hand', 'face', 'mouth', 'eyes', 'eyebrows', 'pupils',
'left_eyebrow', 'left_eye', 'left_pupil', 'right_eyebrow', 'right_eye', 'right_pupil',
'short_sleeved_shirt', 'long_sleeved_shirt', 'short_sleeved_outwear', 'long_sleeved_outwear',
'vest', 'sling', 'shorts', 'trousers', 'skirt', 'short_sleeved_dress', 'long_sleeved_dress', 'vest_dress', 'sling_dress',
"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat",
"traffic light", "fire hydrant", "stop sign", "parking meter", "bench",
"bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
"backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard",
"sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl",
"banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza",
"donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet",
"tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven",
"toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
"hair drier", "toothbrush"
]

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import logging
import impact.core as core
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", 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"], 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})
}
}
RETURN_TYPES = ("MASK",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
def doit(self, sam_model, segs, image, detection_hint, dilation,
threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
return (core.make_sam_mask(sam_model, segs, image, detection_hint, dilation,
threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative), )
class SAMDetectorSegmented:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"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"], 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, "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})
}
}
RETURN_TYPES = ("MASK", "MASK")
RETURN_NAMES = ("combined_mask", "batch_masks")
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
def doit(self, sam_model, segs, image, detection_hint, dilation,
threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
combined_mask, batch_masks = core.make_sam_mask_segmented(sam_model, segs, image, detection_hint, dilation,
threshold, bbox_expansion, mask_hint_threshold,
mask_hint_use_negative)
return (combined_mask, batch_masks, )
class BboxDetectorForEach:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bbox_detector": ("BBOX_DETECTOR", ),
"image": ("IMAGE", ),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}),
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
"labels": ("STRING", {"multiline": True, "default": "all", "placeholder": "List the types of segments to be allowed, separated by commas"}),
},
"optional": {"detailer_hook": ("DETAILER_HOOK",), }
}
RETURN_TYPES = ("SEGS", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
def doit(self, bbox_detector, image, threshold, dilation, crop_factor, drop_size, labels=None, detailer_hook=None):
if len(image) > 1:
raise Exception('[Impact Pack] ERROR: BboxDetectorForEach 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.')
segs = bbox_detector.detect(image, threshold, dilation, crop_factor, drop_size, detailer_hook)
if labels is not None and labels != '':
labels = labels.split(',')
if len(labels) > 0:
segs, _ = segs_nodes.SEGSLabelFilter.filter(segs, labels)
return (segs, )
class SegmDetectorForEach:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"segm_detector": ("SEGM_DETECTOR", ),
"image": ("IMAGE", ),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}),
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
"labels": ("STRING", {"multiline": True, "default": "all", "placeholder": "List the types of segments to be allowed, separated by commas"}),
},
"optional": {"detailer_hook": ("DETAILER_HOOK",), }
}
RETURN_TYPES = ("SEGS", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
def doit(self, segm_detector, image, threshold, dilation, crop_factor, drop_size, labels=None, detailer_hook=None):
if len(image) > 1:
raise Exception('[Impact Pack] ERROR: SegmDetectorForEach 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.')
segs = segm_detector.detect(image, threshold, dilation, crop_factor, drop_size, detailer_hook)
if labels is not None and labels != '':
labels = labels.split(',')
if len(labels) > 0:
segs, _ = segs_nodes.SEGSLabelFilter.filter(segs, labels)
return (segs, )
class SegmDetectorCombined:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"segm_detector": ("SEGM_DETECTOR", ),
"image": ("IMAGE", ),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
}
}
RETURN_TYPES = ("MASK",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
def doit(self, segm_detector, image, threshold, dilation):
mask = segm_detector.detect_combined(image, threshold, dilation)
if mask is None:
mask = torch.zeros((image.shape[1], image.shape[2]), dtype=torch.float32, device="cpu")
return (mask.unsqueeze(0),)
class BboxDetectorCombined(SegmDetectorCombined):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bbox_detector": ("BBOX_DETECTOR", ),
"image": ("IMAGE", ),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"dilation": ("INT", {"default": 4, "min": -512, "max": 512, "step": 1}),
}
}
def doit(self, bbox_detector, image, threshold, dilation):
mask = bbox_detector.detect_combined(image, threshold, dilation)
if mask is None:
mask = torch.zeros((image.shape[1], image.shape[2]), dtype=torch.float32, device="cpu")
return (mask.unsqueeze(0),)
class SimpleDetectorForEach:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bbox_detector": ("BBOX_DETECTOR", ),
"image": ("IMAGE", ),
"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"bbox_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
"sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"sub_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
"sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
"sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional": {
"post_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
"sam_model_opt": ("SAM_MODEL", SAM_MODEL_TOOLTIP_OPTIONAL),
"segm_detector_opt": ("SEGM_DETECTOR", ),
}
}
RETURN_TYPES = ("SEGS",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
@staticmethod
def detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
sub_threshold, sub_dilation, sub_bbox_expansion,
sam_mask_hint_threshold, post_dilation=0, sam_model_opt=None, segm_detector_opt=None,
detailer_hook=None):
if len(image) > 1:
raise Exception('[Impact Pack] ERROR: SimpleDetectorForEach 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.')
if segm_detector_opt is not None and hasattr(segm_detector_opt, 'bbox_detector') and segm_detector_opt.bbox_detector == bbox_detector:
# Better segm support for YOLO-World detector
segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size, detailer_hook=detailer_hook)
else:
segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, crop_factor, drop_size, detailer_hook=detailer_hook)
if sam_model_opt is not None:
mask = core.make_sam_mask(sam_model_opt, segs, image, "center-1", sub_dilation,
sub_threshold, sub_bbox_expansion, sam_mask_hint_threshold, False)
segs = core.segs_bitwise_and_mask(segs, mask)
elif segm_detector_opt is not None:
segm_segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size, detailer_hook=detailer_hook)
mask = core.segs_to_combined_mask(segm_segs)
segs = core.segs_bitwise_and_mask(segs, mask)
segs = core.dilate_segs(segs, post_dilation)
return (segs,)
def doit(self, bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
sub_threshold, sub_dilation, sub_bbox_expansion,
sam_mask_hint_threshold, post_dilation=0, sam_model_opt=None, segm_detector_opt=None):
return SimpleDetectorForEach.detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
sub_threshold, sub_dilation, sub_bbox_expansion,
sam_mask_hint_threshold, post_dilation=post_dilation,
sam_model_opt=sam_model_opt, segm_detector_opt=segm_detector_opt)
class SimpleDetectorForEachPipe:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"detailer_pipe": ("DETAILER_PIPE", ),
"image": ("IMAGE", ),
"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"bbox_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
"sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"sub_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
"sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
"sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional": {
"post_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
}
}
RETURN_TYPES = ("SEGS",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
def doit(self, detailer_pipe, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold, post_dilation=0):
if len(image) > 1:
raise Exception('[Impact Pack] ERROR: SimpleDetectorForEach 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.')
model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, refiner_model, refiner_clip, refiner_positive, refiner_negative = detailer_pipe
return SimpleDetectorForEach.detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
sub_threshold, sub_dilation, sub_bbox_expansion,
sam_mask_hint_threshold, post_dilation=post_dilation, sam_model_opt=sam_model_opt, segm_detector_opt=segm_detector_opt,
detailer_hook=detailer_hook)
class SAM2VideoDetectorSEGS:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image_frames": ("IMAGE", ),
"bbox_detector": ("BBOX_DETECTOR", ),
"sam2_model": ("SAM_MODEL", ),
"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"sam2_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
}
}
RETURN_TYPES = ("SEGS", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
@staticmethod
def doit(bbox_detector, sam2_model, image_frames, bbox_threshold, sam2_threshold, crop_factor, drop_size):
# ---- Check SAM2 model ----
if not isinstance(sam2_model, core.SAM2Wrapper):
logging.error("[Impact Pack] To use the SAM2VideoDetectorSEGS node, a valid SAM2 model must be provided as input to `sam2_model`.")
raise Exception("To use the SAM2VideoDetectorSEGS node, a SAM2 model must be provided as input to `sam2_model`.")
# ---- Detect bboxes ----
segs = bbox_detector.detect(image_frames[0].unsqueeze(0), bbox_threshold, 0, 0, drop_size)
# ---- If no detections, try reversed frames before giving up ----
if len(segs[1]) == 0:
reversed_frames = torch.flip(image_frames, dims=[0])
segs_rev = bbox_detector.detect(reversed_frames[0].unsqueeze(0), bbox_threshold, 0, 0, drop_size)
if len(segs_rev[1]) == 0:
# No Bboxes when reversed -> Give up
h, w = image_frames.shape[1:3]
return (((h, w), []), )
# ---- Predict masks in reversed mode ----
segs_masks = sam2_model.predict_video_segs(reversed_frames, segs_rev)
# segs_masks wieder umdrehen, damit sie mit Originalframes matchen
for k in segs_masks.keys():
segs_masks[k] = torch.flip(segs_masks[k], dims=[0])
else:
# ---- Predict masks if BBOXES were found in forward pass----
segs_masks = sam2_model.predict_video_segs(image_frames, segs)
def get_whole_merged_mask(all_masks):
merged_mask = (all_masks[0] * 255).to(torch.uint8)
for mask in all_masks[1:]:
merged_mask |= (mask * 255).to(torch.uint8)
merged_mask = (merged_mask / 255.0).to(torch.float32)
merged_mask = utils.to_binary_mask(merged_mask, 0.1)[0]
return merged_mask
new_segs = []
for k, v in segs_masks.items():
v = v.squeeze(3)
m = get_whole_merged_mask(v)
seg = segs_nodes.MaskToSEGS.doit(m, False, crop_factor, False, drop_size, contour_fill=True)[0][1]
if len(seg) == 0:
continue
seg = seg[0]
x1, y1, x2, y2 = seg.crop_region
masks = []
for mask in v:
masks.append(mask[y1:y2, x1:x2])
cropped_mask = torch.stack(masks)
cropped_mask = (cropped_mask >= (sam2_threshold * 100 - 50)).to(torch.uint8).cpu()
new_seg = SEG(
seg.cropped_image,
cropped_mask,
seg.confidence,
seg.crop_region,
seg.bbox,
seg.label,
seg.control_net_wrapper
)
new_segs.append(new_seg)
return ((segs[0], new_segs), )
class SimpleDetectorForAnimateDiff:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bbox_detector": ("BBOX_DETECTOR", ),
"image_frames": ("IMAGE", ),
"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"bbox_dilation": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
"sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"sub_dilation": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
"sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
"sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"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_TOOLTIP_OPTIONAL),
"segm_detector_opt": ("SEGM_DETECTOR", ),
}
}
RETURN_TYPES = ("SEGS",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
@staticmethod
def detect(bbox_detector, image_frames, bbox_threshold, bbox_dilation, crop_factor, drop_size,
sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold,
masking_mode="Pivot SEGS", segs_pivot="Combined mask", sam_model_opt=None, segm_detector_opt=None):
h = image_frames.shape[1]
w = image_frames.shape[2]
# gather segs for all frames
segs_by_frames = []
for image in image_frames:
image = image.unsqueeze(0)
segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, crop_factor, drop_size)
if sam_model_opt is not None:
mask = core.make_sam_mask(sam_model_opt, segs, image, "center-1", sub_dilation,
sub_threshold, sub_bbox_expansion, sam_mask_hint_threshold, False)
segs = core.segs_bitwise_and_mask(segs, mask)
elif segm_detector_opt is not None:
segm_segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size)
mask = core.segs_to_combined_mask(segm_segs)
segs = core.segs_bitwise_and_mask(segs, mask)
segs_by_frames.append(segs)
def get_masked_frames():
masks_by_frame = []
for i, segs in enumerate(segs_by_frames):
masks_in_frame = segs_nodes.SEGSToMaskList().doit(segs)[0]
current_frame_mask = (masks_in_frame[0] * 255).to(torch.uint8)
for mask in masks_in_frame[1:]:
current_frame_mask |= (mask * 255).to(torch.uint8)
current_frame_mask = (current_frame_mask/255.0).to(torch.float32)
current_frame_mask = utils.to_binary_mask(current_frame_mask, 0.1)[0]
masks_by_frame.append(current_frame_mask)
return masks_by_frame
def get_empty_mask():
return torch.zeros((h, w), dtype=torch.float32, device="cpu")
def get_neighboring_mask_at(i, masks_by_frame):
prv = masks_by_frame[i-1] if i > 1 else get_empty_mask()
cur = masks_by_frame[i]
nxt = masks_by_frame[i-1] if i > 1 else get_empty_mask()
prv = prv if prv is not None else get_empty_mask()
cur = cur.clone() if cur is not None else get_empty_mask()
nxt = nxt if nxt is not None else get_empty_mask()
return prv, cur, nxt
def get_merged_neighboring_mask(masks_by_frame):
if len(masks_by_frame) <= 1:
return masks_by_frame
result = []
for i in range(0, len(masks_by_frame)):
prv, cur, nxt = get_neighboring_mask_at(i, masks_by_frame)
cur = (cur * 255).to(torch.uint8)
cur |= (prv * 255).to(torch.uint8)
cur |= (nxt * 255).to(torch.uint8)
cur = (cur / 255.0).to(torch.float32)
cur = utils.to_binary_mask(cur, 0.1)[0]
result.append(cur)
return result
def get_whole_merged_mask():
all_masks = []
for segs in segs_by_frames:
all_masks += segs_nodes.SEGSToMaskList().doit(segs)[0]
merged_mask = (all_masks[0] * 255).to(torch.uint8)
for mask in all_masks[1:]:
merged_mask |= (mask * 255).to(torch.uint8)
merged_mask = (merged_mask / 255.0).to(torch.float32)
merged_mask = utils.to_binary_mask(merged_mask, 0.1)[0]
return merged_mask
def get_pivot_segs():
if segs_pivot == "1st frame mask":
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]
def get_segs(merged_neighboring=False):
pivot_segs = get_pivot_segs()
masks_by_frame = get_masked_frames()
if merged_neighboring:
masks_by_frame = get_merged_neighboring_mask(masks_by_frame)
new_segs = []
for seg in pivot_segs[1]:
cropped_mask = torch.zeros(seg.cropped_mask.shape, dtype=torch.float32, device="cpu").unsqueeze(0)
pivot_mask = torch.from_numpy(seg.cropped_mask)
x1, y1, x2, y2 = seg.crop_region
for mask in masks_by_frame:
cropped_mask_at_frame = (mask[y1:y2, x1:x2] * pivot_mask).unsqueeze(0)
cropped_mask = torch.cat((cropped_mask, cropped_mask_at_frame), dim=0)
if len(cropped_mask) > 1:
cropped_mask = cropped_mask[1:]
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_segs(merged_neighboring=True), )
else: # elif masking_mode == "Don't combine":
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,
masking_mode="Pivot SEGS", segs_pivot="Combined mask", sam_model_opt=None, segm_detector_opt=None):
return SimpleDetectorForAnimateDiff.detect(bbox_detector, image_frames, bbox_threshold, bbox_dilation, crop_factor, drop_size,
sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold,
masking_mode, segs_pivot, sam_model_opt, segm_detector_opt)

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custom_nodes/ComfyUI-Impact-Pack/modules/impact/hf_nodes.py Normal file
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import comfy
import re
from impact import utils
hf_transformer_model_urls = [
"rizvandwiki/gender-classification-2",
"NTQAI/pedestrian_gender_recognition",
"Leilab/gender_class",
"ProjectPersonal/GenderClassifier",
"crangana/trained-gender",
"cledoux42/GenderNew_v002",
"ivensamdh/genderage2"
]
class HF_TransformersClassifierProvider:
@classmethod
def INPUT_TYPES(s):
global hf_transformer_model_urls
return {"required": {
"preset_repo_id": (hf_transformer_model_urls + ['Manual repo id'],),
"manual_repo_id": ("STRING", {"multiline": False}),
"device_mode": (["AUTO", "Prefer GPU", "CPU"],),
},
}
RETURN_TYPES = ("TRANSFORMERS_CLASSIFIER",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/HuggingFace"
def doit(self, preset_repo_id, manual_repo_id, device_mode):
from transformers import pipeline
if preset_repo_id == 'Manual repo id':
url = manual_repo_id
else:
url = preset_repo_id
if device_mode != 'CPU':
device = comfy.model_management.get_torch_device()
else:
device = "cpu"
classifier = pipeline('image-classification', model=url, device=device)
return (classifier,)
preset_classify_expr = [
'#Female > #Male',
'#Female < #Male',
'female > 0.5',
'male > 0.5',
'Age16to25 > 0.1',
'Age50to69 > 0.1',
]
symbolic_label_map = {
'#Female': {'female', 'Female', 'Human Female', 'woman', 'women', 'girl'},
'#Male': {'male', 'Male', 'Human Male', 'man', 'men', 'boy'}
}
def is_numeric_string(input_str):
return re.match(r'^-?\d+(\.\d+)?$', input_str) is not None
classify_expr_pattern = r'([^><= ]+)\s*(>|<|>=|<=|=)\s*([^><= ]+)'
class SEGS_Classify:
@classmethod
def INPUT_TYPES(s):
global preset_classify_expr
return {"required": {
"classifier": ("TRANSFORMERS_CLASSIFIER",),
"segs": ("SEGS",),
"preset_expr": (preset_classify_expr + ['Manual expr'],),
"manual_expr": ("STRING", {"multiline": False}),
},
"optional": {
"ref_image_opt": ("IMAGE", ),
}
}
RETURN_TYPES = ("SEGS", "SEGS", "STRING")
RETURN_NAMES = ("filtered_SEGS", "remained_SEGS", "detected_labels")
OUTPUT_IS_LIST = (False, False, True)
FUNCTION = "doit"
CATEGORY = "ImpactPack/HuggingFace"
@staticmethod
def lookup_classified_label_score(score_infos, label):
global symbolic_label_map
if label.startswith('#'):
if label not in symbolic_label_map:
return None
else:
label = symbolic_label_map[label]
else:
label = {label}
for x in score_infos:
if x['label'] in label:
return x['score']
return None
def doit(self, classifier, segs, preset_expr, manual_expr, ref_image_opt=None):
if preset_expr == 'Manual expr':
expr_str = manual_expr
else:
expr_str = preset_expr
match = re.match(classify_expr_pattern, expr_str)
if match is None:
return (segs[0], []), segs, []
a = match.group(1)
op = match.group(2)
b = match.group(3)
a_is_lab = not is_numeric_string(a)
b_is_lab = not is_numeric_string(b)
classified = []
remained_SEGS = []
provided_labels = set()
for seg in segs[1]:
cropped_image = None
if seg.cropped_image is not None:
cropped_image = seg.cropped_image
elif ref_image_opt is not None:
# take from original image
cropped_image = utils.crop_image(ref_image_opt, seg.crop_region)
if cropped_image is not None:
cropped_image = utils.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)
filtered_SEGS = []
for seg, res in classified:
if a_is_lab:
avalue = SEGS_Classify.lookup_classified_label_score(res, a)
else:
avalue = a
if b_is_lab:
bvalue = SEGS_Classify.lookup_classified_label_score(res, b)
else:
bvalue = b
if avalue is None or bvalue is None:
remained_SEGS.append(seg)
continue
avalue = float(avalue)
bvalue = float(bvalue)
if op == '>':
cond = avalue > bvalue
elif op == '<':
cond = avalue < bvalue
elif op == '>=':
cond = avalue >= bvalue
elif op == '<=':
cond = avalue <= bvalue
else:
cond = avalue == bvalue
if cond:
filtered_SEGS.append(seg)
else:
remained_SEGS.append(seg)
return (segs[0], filtered_SEGS), (segs[0], remained_SEGS), list(provided_labels)

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import sys
from . import hooks
from . import defs
class SEGSOrderedFilterDetailerHookProvider:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2"],),
"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}),
},
}
RETURN_TYPES = ("DETAILER_HOOK", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, target, order, take_start, take_count):
hook = hooks.SEGSOrderedFilterDetailerHook(target, order, take_start, take_count)
return (hook, )
class SEGSRangeFilterDetailerHookProvider:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2", "length_percent"],),
"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}),
},
}
RETURN_TYPES = ("DETAILER_HOOK", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, target, mode, min_value, max_value):
hook = hooks.SEGSRangeFilterDetailerHook(target, mode, min_value, max_value)
return (hook, )
class SEGSLabelFilterDetailerHookProvider:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"segs": ("SEGS", ),
"preset": (['all'] + defs.detection_labels,),
"labels": ("STRING", {"multiline": True, "placeholder": "List the types of segments to be allowed, separated by commas"}),
},
}
RETURN_TYPES = ("DETAILER_HOOK", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, preset, labels):
hook = hooks.SEGSLabelFilterDetailerHook(labels)
return (hook, )
class PreviewDetailerHookProvider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {"quality": ("INT", {"default": 95, "min": 20, "max": 100})},
"hidden": {"unique_id": "UNIQUE_ID"},
}
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, hook
class LamaRemoverDetailerHookProvider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask_threshold":("INT", {"default": 250, "min": 0, "max": 255, "step": 1, "display": "slider"}),
"gaussblur_radius": ("INT", {"default": 8, "min": 0, "max": 20, "step": 1, "display": "slider"}),
"skip_sampling": ("BOOLEAN", {"default": True}),
}
}
RETURN_TYPES = ("DETAILER_HOOK", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, mask_threshold, gaussblur_radius, skip_sampling):
hook = hooks.LamaRemoverDetailerHook(mask_threshold, gaussblur_radius, skip_sampling)
return (hook, )
class BlackPatchRetryHookProvider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mean_thresh": ("INT", {"default": 10, "min": 0, "max": 255}),
"var_thresh": ("INT", {"default": 5, "min": 0, "max": 255})
},
}
RETURN_TYPES = ("DETAILER_HOOK", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
NOT_IDEMPOTENT = True
def doit(self, mean_thresh, var_thresh):
hook = hooks.BlackPatchRetryHook(mean_thresh, var_thresh)
return hook,

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import copy
import torch
import nodes
from impact import utils
from . import segs_nodes
from thirdparty import noise_nodes
from server import PromptServer
import asyncio
import folder_paths
import os
from comfy_extras import nodes_custom_sampler
import math
import logging
class PixelKSampleHook:
cur_step = 0
total_step = 0
def __init__(self):
pass
def set_steps(self, info):
self.cur_step, self.total_step = info
def post_decode(self, pixels):
return pixels
def post_upscale(self, pixels, mask=None):
return pixels
def post_encode(self, samples):
return samples
def pre_decode(self, samples):
return samples
def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent,
denoise):
return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise
def post_crop_region(self, w, h, item_bbox, crop_region):
return crop_region
def touch_scaled_size(self, w, h):
return w, h
class PixelKSampleHookCombine(PixelKSampleHook):
hook1 = None
hook2 = None
def __init__(self, hook1, hook2):
super().__init__()
self.hook1 = hook1
self.hook2 = hook2
def set_steps(self, info):
self.hook1.set_steps(info)
self.hook2.set_steps(info)
def pre_decode(self, samples):
return self.hook2.pre_decode(self.hook1.pre_decode(samples))
def post_decode(self, pixels):
return self.hook2.post_decode(self.hook1.post_decode(pixels))
def post_upscale(self, pixels, mask=None):
return self.hook2.post_upscale(self.hook1.post_upscale(pixels, mask), mask)
def post_encode(self, samples):
return self.hook2.post_encode(self.hook1.post_encode(samples))
def post_crop_region(self, w, h, item_bbox, crop_region):
crop_region = self.hook1.post_crop_region(w, h, item_bbox, crop_region)
return self.hook2.post_crop_region(w, h, item_bbox, crop_region)
def touch_scaled_size(self, w, h):
w, h = self.hook1.touch_scaled_size(w, h)
return self.hook2.touch_scaled_size(w, h)
def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent,
denoise):
model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \
self.hook1.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
upscaled_latent, denoise)
return self.hook2.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
upscaled_latent, denoise)
class DetailerHookCombine(PixelKSampleHookCombine):
def cycle_latent(self, latent):
latent = self.hook1.cycle_latent(latent)
latent = self.hook2.cycle_latent(latent)
return latent
def post_detection(self, segs):
segs = self.hook1.post_detection(segs)
segs = self.hook2.post_detection(segs)
return segs
def post_paste(self, image):
image = self.hook1.post_paste(image)
image = self.hook2.post_paste(image)
return image
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
def get_custom_sampler(self):
if self.hook1.get_custom_sampler() is not None:
return self.hook1.get_custom_sampler()
else:
return self.hook2.get_custom_sampler()
def get_skip_sampling(self):
return self.hook1.get_skip_sampling() and self.hook2.get_skip_sampling()
def should_retry_patch(self, patch):
return self.hook1.should_retry_patch(patch) or self.hook2.should_retry_patch(patch)
class SimpleCfgScheduleHook(PixelKSampleHook):
target_cfg = 0
def __init__(self, target_cfg):
super().__init__()
self.target_cfg = target_cfg
def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise):
if self.total_step > 1:
progress = self.cur_step / (self.total_step - 1)
gap = self.target_cfg - cfg
current_cfg = int(cfg + gap * progress)
else:
current_cfg = self.target_cfg
return model, seed, steps, current_cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise
class SimpleDenoiseScheduleHook(PixelKSampleHook):
def __init__(self, target_denoise):
super().__init__()
self.target_denoise = target_denoise
def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise):
if self.total_step > 1:
progress = self.cur_step / (self.total_step - 1)
gap = self.target_denoise - denoise
current_denoise = denoise + gap * progress
else:
current_denoise = self.target_denoise
return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, current_denoise
class SimpleStepsScheduleHook(PixelKSampleHook):
def __init__(self, target_steps):
super().__init__()
self.target_steps = target_steps
def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise):
if self.total_step > 1:
progress = self.cur_step / (self.total_step - 1)
gap = self.target_steps - steps
current_steps = int(steps + gap * progress)
else:
current_steps = self.target_steps
return model, seed, current_steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise
class DetailerHook(PixelKSampleHook):
def cycle_latent(self, latent):
return latent
def post_detection(self, segs):
return segs
def post_paste(self, image):
return image
def get_custom_noise(self, seed, noise, is_touched):
return noise, is_touched
def get_custom_sampler(self):
return None
def get_skip_sampling(self):
return False
def should_retry_patch(self, patch):
return False
class CustomSamplerDetailerHookProvider(DetailerHook):
def __init__(self, sampler):
super().__init__()
self.sampler = sampler
def get_custom_sampler(self):
return self.sampler
# class CustomNoiseDetailerHookProvider(DetailerHook):
# def __init__(self, noise):
# 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.variation_seed = variation_seed
self.variation_strength = variation_strength
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)
mixed_noise = ((1 - self.variation_strength) * noise + self.variation_strength * noise_2nd)
# 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
return corrected_noise, True
class SimpleDetailerDenoiseSchedulerHook(DetailerHook):
def __init__(self, target_denoise):
super().__init__()
self.target_denoise = target_denoise
def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise):
if self.total_step > 1:
progress = self.cur_step / (self.total_step - 1)
gap = self.target_denoise - denoise
current_denoise = denoise + gap * progress
else:
# ignore hook if total cycle <= 1
current_denoise = denoise
return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, current_denoise
class CoreMLHook(DetailerHook):
def __init__(self, mode):
super().__init__()
resolution = mode.split('x')
self.w = int(resolution[0])
self.h = int(resolution[1])
self.override_bbox_by_segm = False
def pre_decode(self, samples):
new_samples = copy.deepcopy(samples)
new_samples['samples'] = samples['samples'][0].unsqueeze(0)
return new_samples
def post_encode(self, samples):
new_samples = copy.deepcopy(samples)
new_samples['samples'] = samples['samples'].repeat(2, 1, 1, 1)
return new_samples
def post_crop_region(self, w, h, item_bbox, crop_region):
x1, y1, x2, y2 = crop_region
bx1, by1, bx2, by2 = item_bbox
crop_w = x2-x1
crop_h = y2-y1
crop_ratio = crop_w/crop_h
target_ratio = self.w/self.h
if crop_ratio < target_ratio:
# shrink height
top_gap = by1 - y1
bottom_gap = y2 - by2
gap_ratio = top_gap / bottom_gap
target_height = 1/target_ratio*crop_w
delta_height = crop_h - target_height
new_y1 = int(y1 + delta_height*gap_ratio)
new_y2 = int(new_y1 + target_height)
crop_region = x1, new_y1, x2, new_y2
elif crop_ratio > target_ratio:
# shrink width
left_gap = bx1 - x1
right_gap = x2 - bx2
gap_ratio = left_gap / right_gap
target_width = target_ratio*crop_h
delta_width = crop_w - target_width
new_x1 = int(x1 + delta_width*gap_ratio)
new_x2 = int(new_x1 + target_width)
crop_region = new_x1, y1, new_x2, y2
return crop_region
def touch_scaled_size(self, w, h):
return self.w, self.h
# REQUIREMENTS: BlenderNeko/ComfyUI Noise
class InjectNoiseHook(PixelKSampleHook):
def __init__(self, source, seed, start_strength, end_strength):
super().__init__()
self.source = source
self.seed = seed
self.start_strength = start_strength
self.end_strength = end_strength
def post_encode(self, samples):
cur_step = self.cur_step
size = samples['samples'].shape
seed = cur_step + self.seed + cur_step
if "BNK_NoisyLatentImage" in nodes.NODE_CLASS_MAPPINGS and "BNK_InjectNoise" in nodes.NODE_CLASS_MAPPINGS:
NoisyLatentImage = nodes.NODE_CLASS_MAPPINGS["BNK_NoisyLatentImage"]
InjectNoise = nodes.NODE_CLASS_MAPPINGS["BNK_InjectNoise"]
else:
utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_Noise',
"To use 'NoiseInjectionHookProvider', 'ComfyUI Noise' extension is required.")
raise Exception("'BNK_NoisyLatentImage', 'BNK_InjectNoise' nodes are not installed.")
noise = NoisyLatentImage().create_noisy_latents(self.source, seed, size[3] * 8, size[2] * 8, size[0])[0]
# inj noise
mask = None
if 'noise_mask' in samples:
mask = samples['noise_mask']
strength = self.start_strength + (self.end_strength - self.start_strength) * cur_step / self.total_step
samples = InjectNoise().inject_noise(samples, strength, noise, mask)[0]
logging.info(f"[Impact Pack] InjectNoiseHook: strength = {strength}")
if mask is not None:
samples['noise_mask'] = mask
return samples
class UnsamplerHook(PixelKSampleHook):
def __init__(self, model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name,
scheduler, normalize, positive, negative):
super().__init__()
self.model = model
self.cfg = cfg
self.sampler_name = sampler_name
self.steps = steps
self.start_end_at_step = start_end_at_step
self.end_end_at_step = end_end_at_step
self.scheduler = scheduler
self.normalize = normalize
self.positive = positive
self.negative = negative
def post_encode(self, samples):
cur_step = self.cur_step
Unsampler = noise_nodes.Unsampler
end_at_step = self.start_end_at_step + (self.end_end_at_step - self.start_end_at_step) * cur_step / self.total_step
end_at_step = int(end_at_step)
logging.info(f"[Impact Pack] UnsamplerHook: end_at_step = {end_at_step}")
# inj noise
mask = None
if 'noise_mask' in samples:
mask = samples['noise_mask']
samples = Unsampler().unsampler(self.model, self.cfg, self.sampler_name, self.steps, end_at_step,
self.scheduler, self.normalize, self.positive, self.negative, samples)[0]
if mask is not None:
samples['noise_mask'] = mask
return samples
class InjectNoiseHookForDetailer(DetailerHook):
def __init__(self, source, seed, start_strength, end_strength, from_start=False):
super().__init__()
self.source = source
self.seed = seed
self.start_strength = start_strength
self.end_strength = end_strength
self.from_start = from_start
def inject_noise(self, samples):
cur_step = self.cur_step if self.from_start else self.cur_step - 1
total_step = self.total_step if self.from_start else self.total_step - 1
size = samples['samples'].shape
seed = cur_step + self.seed + cur_step
if "BNK_NoisyLatentImage" in nodes.NODE_CLASS_MAPPINGS and "BNK_InjectNoise" in nodes.NODE_CLASS_MAPPINGS:
NoisyLatentImage = nodes.NODE_CLASS_MAPPINGS["BNK_NoisyLatentImage"]
InjectNoise = nodes.NODE_CLASS_MAPPINGS["BNK_InjectNoise"]
else:
utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_Noise',
"To use 'NoiseInjectionDetailerHookProvider', 'ComfyUI Noise' extension is required.")
raise Exception("'BNK_NoisyLatentImage', 'BNK_InjectNoise' nodes are not installed.")
noise = NoisyLatentImage().create_noisy_latents(self.source, seed, size[3] * 8, size[2] * 8, size[0])[0]
# inj noise
mask = None
if 'noise_mask' in samples:
mask = samples['noise_mask']
strength = self.start_strength + (self.end_strength - self.start_strength) * cur_step / total_step
samples = InjectNoise().inject_noise(samples, strength, noise, mask)[0]
if mask is not None:
samples['noise_mask'] = mask
return samples
def cycle_latent(self, latent):
if self.cur_step == 0 and not self.from_start:
return latent
else:
return self.inject_noise(latent)
class UnsamplerDetailerHook(DetailerHook):
def __init__(self, model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name,
scheduler, normalize, positive, negative, from_start=False):
super().__init__()
self.model = model
self.cfg = cfg
self.sampler_name = sampler_name
self.steps = steps
self.start_end_at_step = start_end_at_step
self.end_end_at_step = end_end_at_step
self.scheduler = scheduler
self.normalize = normalize
self.positive = positive
self.negative = negative
self.from_start = from_start
def unsample(self, samples):
cur_step = self.cur_step if self.from_start else self.cur_step - 1
total_step = self.total_step if self.from_start else self.total_step - 1
Unsampler = noise_nodes.Unsampler
end_at_step = self.start_end_at_step + (self.end_end_at_step - self.start_end_at_step) * cur_step / total_step
end_at_step = int(end_at_step)
# inj noise
mask = None
if 'noise_mask' in samples:
mask = samples['noise_mask']
samples = Unsampler().unsampler(self.model, self.cfg, self.sampler_name, self.steps, end_at_step,
self.scheduler, self.normalize, self.positive, self.negative, samples)[0]
if mask is not None:
samples['noise_mask'] = mask
return samples
def cycle_latent(self, latent):
if self.cur_step == 0 and not self.from_start:
return latent
else:
return self.unsample(latent)
class SEGSOrderedFilterDetailerHook(DetailerHook):
def __init__(self, target, order, take_start, take_count):
super().__init__()
self.target = target
self.order = order
self.take_start = take_start
self.take_count = take_count
def post_detection(self, segs):
return segs_nodes.SEGSOrderedFilter().doit(segs, self.target, self.order, self.take_start, self.take_count)[0]
class SEGSRangeFilterDetailerHook(DetailerHook):
def __init__(self, target, mode, min_value, max_value):
super().__init__()
self.target = target
self.mode = mode
self.min_value = min_value
self.max_value = max_value
def post_detection(self, segs):
return segs_nodes.SEGSRangeFilter().doit(segs, self.target, self.mode, self.min_value, self.max_value)[0]
class SEGSLabelFilterDetailerHook(DetailerHook):
def __init__(self, labels):
super().__init__()
self.labels = labels
def post_detection(self, segs):
return segs_nodes.SEGSLabelFilter().doit(segs, "", self.labels)[0]
class LamaRemoverDetailerHook(DetailerHook):
def __init__(self, mask_threshold, gaussblur_radius, skip_sampling):
super().__init__()
self.mask_threshold = mask_threshold
self.gaussblur_radius = gaussblur_radius
self.skip_sampling = skip_sampling
def post_upscale(self, img, mask=None):
if "LamaRemover" in nodes.NODE_CLASS_MAPPINGS:
lama_remover_obj = nodes.NODE_CLASS_MAPPINGS['LamaRemover']()
else:
utils.try_install_custom_node('https://github.com/Layer-norm/comfyui-lama-remover',
"To use 'LAMARemoverDetailerHookProvider', 'comfyui-lama-remover' nodepack is required.")
raise Exception("'LamaRemover' node is not installed.")
return lama_remover_obj.lama_remover(img, masks=mask, mask_threshold=self.mask_threshold, gaussblur_radius=self.gaussblur_radius, invert_mask=False)[0]
def get_skip_sampling(self):
return self.skip_sampling
class PreviewDetailerHook(DetailerHook):
def __init__(self, node_id, quality):
super().__init__()
self.node_id = node_id
self.quality = quality
async def send(self, image):
if len(image) > 0:
image = image[0].unsqueeze(0)
img = utils.tensor2pil(image)
temp_path = os.path.join(folder_paths.get_temp_directory(), 'pvhook')
if not os.path.exists(temp_path):
os.makedirs(temp_path)
fullpath = os.path.join(temp_path, f"{self.node_id}.webp")
img.save(fullpath, quality=self.quality)
item = {
"filename": f"{self.node_id}.webp",
"subfolder": 'pvhook',
"type": 'temp'
}
PromptServer.instance.send_sync("impact-preview", {'node_id': self.node_id, 'item': item})
def post_paste(self, image):
loop = asyncio.get_running_loop()
loop.create_task(self.send(image))
return image
class BlackPatchRetryHook(DetailerHook):
def __init__(self, mean_thresh, var_thresh):
super().__init__()
assert 0 <= mean_thresh <= 255 and 0 <= var_thresh <= 255
self.mean_thresh = mean_thresh
self.var_thresh = var_thresh
def should_retry_patch(self, cropped_region):
# remove the first dimension (batch_size)
if cropped_region.ndim == 4:
assert cropped_region.shape[0] == 1
cropped_region = cropped_region.squeeze(0)
# turn image to grayscape
if cropped_region.ndim == 3:
assert cropped_region.shape[-1] in [1, 3]
cropped_region = cropped_region.mean(axis=-1) # simple average grayscale
mean = cropped_region.mean()
var = cropped_region.var()
return (mean <= self.mean_thresh/255) and (var <= self.var_thresh/255)

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import impact.additional_dependencies
import numpy as np
from impact import utils
import logging
impact.additional_dependencies.ensure_onnx_package()
try:
import onnxruntime
def onnx_inference(image, onnx_model):
# prepare image
pil = utils.tensor2pil(image)
image = np.ascontiguousarray(pil)
image = image[:, :, ::-1] # to BGR image
image = image.astype(np.float32)
image -= [103.939, 116.779, 123.68] # 'caffe' mode image preprocessing
# do detection
onnx_model = onnxruntime.InferenceSession(onnx_model, providers=["CPUExecutionProvider"])
outputs = onnx_model.run(
[s_i.name for s_i in onnx_model.get_outputs()],
{onnx_model.get_inputs()[0].name: np.expand_dims(image, axis=0)},
)
labels = [op for op in outputs if op.dtype == "int32"][0]
scores = [op for op in outputs if isinstance(op[0][0], np.float32)][0]
boxes = [op for op in outputs if isinstance(op[0][0], np.ndarray)][0]
# filter-out useless item
idx = np.where(labels[0] == -1)[0][0]
labels = labels[0][:idx]
scores = scores[0][:idx]
boxes = boxes[0][:idx].astype(np.uint32)
return labels, scores, boxes
except Exception:
logging.error("[Impact Pack] ComfyUI-Impact-Pack: 'onnxruntime' package doesn't support 'python 3.11', yet.\t{e}")

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custom_nodes/ComfyUI-Impact-Pack/modules/impact/impact_sampling.py Normal file
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import logging
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 Exception:
logging.warning("\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):
discard_penultimate_sigma = False
if sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']:
steps += 1
discard_penultimate_sigma = True
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']().execute(float(scheduler[11:-1]), steps, denoise=1.0)[0]
elif scheduler == 'LTXV[default]':
sigmas = nodes.NODE_CLASS_MAPPINGS['LTXVScheduler']().execute(20, 2.05, 0.95, True, 0.1)[0]
elif scheduler.startswith('OSS'):
sigmas = nodes.NODE_CLASS_MAPPINGS['OptimalStepsScheduler']().execute(scheduler[4:], steps, denoise=1.0)[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:]])
return sigmas
def get_noise_sampler(x, cpu, total_sigmas, **kwargs):
if 'extra_args' in kwargs and 'seed' in kwargs['extra_args']:
sigma_min, sigma_max = total_sigmas[total_sigmas > 0].min(), total_sigmas.max()
seed = kwargs['extra_args'].get("seed", None)
return k_diffusion_sampling.BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=cpu)
return None
def ksampler(sampler_name, total_sigmas, extra_options={}, inpaint_options={}):
if sampler_name in ["dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu"]:
if sampler_name == "dpmpp_sde":
orig_sampler_function = k_diffusion_sampling.sample_dpmpp_sde
elif sampler_name == "dpmpp_sde_gpu":
orig_sampler_function = k_diffusion_sampling.sample_dpmpp_sde_gpu
elif sampler_name == "dpmpp_2m_sde":
orig_sampler_function = k_diffusion_sampling.sample_dpmpp_2m_sde
elif sampler_name == "dpmpp_2m_sde_gpu":
orig_sampler_function = k_diffusion_sampling.sample_dpmpp_2m_sde_gpu
elif sampler_name == "dpmpp_3m_sde":
orig_sampler_function = k_diffusion_sampling.sample_dpmpp_3m_sde
elif sampler_name == "dpmpp_3m_sde_gpu":
orig_sampler_function = k_diffusion_sampling.sample_dpmpp_3m_sde_gpu
def sampler_function_wrapper(model, x, sigmas, **kwargs):
if 'noise_sampler' not in kwargs:
kwargs['noise_sampler'] = get_noise_sampler(x, 'gpu' not in sampler_name, total_sigmas, **kwargs)
return orig_sampler_function(model, x, sigmas, **kwargs)
sampler_function = sampler_function_wrapper
else:
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, noise=None, callback=None, scheduler_func=None):
if scheduler_func is not None:
total_sigmas = scheduler_func(model, sampler_name, steps)
else:
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)}
if sampler_opt is None:
impact_sampler = ksampler(sampler_name, total_sigmas)
else:
impact_sampler = sampler_opt
if len(sigmas) == 0 or (len(sigmas) == 1 and sigmas[0] == 0):
return 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]
else:
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, noise=None, scheduler_func=None, sampler_opt=None):
if refiner_ratio is None or refiner_model is None or refiner_clip is None or refiner_positive is None or refiner_negative is None:
# Use separated_sample instead of KSampler for `AYS scheduler`
# 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, sampler_opt=sampler_opt, noise=noise, scheduler_func=scheduler_func)
else:
advanced_steps = math.floor(steps / denoise)
start_at_step = advanced_steps - steps
end_at_step = start_at_step + math.floor(steps * (1.0 - refiner_ratio))
# 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, sampler_opt=sampler_opt, noise=noise, scheduler_func=scheduler_func)
if 'noise_mask' in latent_image:
# noise_latent = \
# impact_sampling.separated_sample(refiner_model, "enable", seed, advanced_steps, cfg, sampler_name,
# scheduler, refiner_positive, refiner_negative, latent_image, end_at_step,
# end_at_step, "enable")
latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
temp_latent = latent_compositor.composite(latent_image, temp_latent, 0, 0, False, latent_image['noise_mask'])[0]
# 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, sampler_opt=sampler_opt, scheduler_func=scheduler_func)
return refined_latent
class KSamplerAdvancedWrapper:
params = None
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, 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)
if hook is not None:
model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent = hook.pre_ksample_advanced(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, start_at_step, end_at_step,
return_with_leftover_noise)
if recovery_mode != 'DISABLE' and sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']:
base_image = latent_image.copy()
if recovery_mode == "ratio between":
sigma_ratio = 1.0 - recovery_sigma_ratio
else:
sigma_ratio = 1.0
else:
base_image = None
sigma_ratio = 1.0
try:
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, noise=noise, scheduler_func=self.scheduler_func)
except ValueError as e:
if str(e) == 'sigma_min and sigma_max must not be 0':
logging.warning("\nWARN: sampling skipped - sigma_min and sigma_max are 0")
return latent_image
if (recovery_sigma_ratio > 0 and recovery_mode != 'DISABLE' and
sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']):
compensate = 0 if sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu'] else 2
if recovery_sampler == "AUTO":
recovery_sampler = 'dpm_fast' if sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu'] else 'dpmpp_2m'
latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
noise_mask = latent_image['noise_mask']
if len(noise_mask.shape) == 4:
noise_mask = noise_mask.squeeze(0).squeeze(0)
latent_image = latent_compositor.composite(base_image, latent_image, 0, 0, False, noise_mask)[0]
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, scheduler_func=self.scheduler_func)
except ValueError as e:
if str(e) == 'sigma_min and sigma_max must not be 0':
logging.warning("\nWARN: sampling skipped - sigma_min and sigma_max are 0")
return latent_image
class KSamplerWrapper:
params = None
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
if hook is not None:
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 impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, scheduler_func=self.scheduler_func)

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import io
import logging
import os
import random
import threading
import traceback
from io import BytesIO
import comfy
import folder_paths
import impact
import impact.core as core
import impact.impact_pack as impact_pack
import impact.utils as utils
import nodes
import numpy as np
import torchvision
from aiohttp import web
from impact.utils import to_tensor
from PIL import Image
from segment_anything import SamPredictor, sam_model_registry
from server import PromptServer
sam_predictor = None
default_sam_model_name = os.path.join(impact_pack.model_path, "sams", "sam_vit_b_01ec64.pth")
sam_lock = threading.Condition()
last_prepare_data = None
def async_prepare_sam(image_dir, model_name, filename):
with sam_lock:
global sam_predictor
if 'vit_h' in model_name:
model_kind = 'vit_h'
elif 'vit_l' in model_name:
model_kind = 'vit_l'
else:
model_kind = 'vit_b'
sam_model = sam_model_registry[model_kind](checkpoint=model_name)
sam_predictor = SamPredictor(sam_model)
image_path = os.path.join(image_dir, filename)
image = nodes.LoadImage().load_image(image_path)[0]
image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
if impact.config.get_config()['sam_editor_cpu']:
device = 'cpu'
else:
device = comfy.model_management.get_torch_device()
sam_predictor.model.to(device=device)
sam_predictor.set_image(image, "RGB")
sam_predictor.model.cpu()
@PromptServer.instance.routes.post("/sam/prepare")
async def sam_prepare(request):
global sam_predictor
global last_prepare_data
data = await request.json()
with sam_lock:
if last_prepare_data is not None and last_prepare_data == data:
# already loaded: skip -- prevent redundant loading
return web.Response(status=200)
last_prepare_data = data
model_name = 'sam_vit_b_01ec64.pth'
if data['sam_model_name'] == 'auto':
model_name = impact.config.get_config()['sam_editor_model']
model_path = folder_paths.get_full_path("sams", model_name)
if model_path is None:
logging.error(f"[Impact Pack] The '{model_name}' model file cannot be found in any sams model path.")
return web.Response(status=400)
logging.info(f"[Impact Pack] Loading SAM model '{model_path}'")
filename, image_dir = folder_paths.annotated_filepath(data["filename"])
if image_dir is None:
typ = data['type'] if data['type'] != '' else 'output'
image_dir = folder_paths.get_directory_by_type(typ)
if data['subfolder'] is not None and data['subfolder'] != '':
image_dir += f"/{data['subfolder']}"
if image_dir is None:
return web.Response(status=400)
thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_path, filename,))
thread.start()
logging.info("[Impact Pack] SAM model loaded. ")
return web.Response(status=200)
@PromptServer.instance.routes.post("/sam/release")
async def release_sam(request):
global sam_predictor
with sam_lock:
temp = sam_predictor
del temp
sam_predictor = None
logging.info("[Impact Pack]: unloading SAM model")
@PromptServer.instance.routes.post("/sam/detect")
async def sam_detect(request):
global sam_predictor
with sam_lock:
if sam_predictor is not None:
if impact.config.get_config()['sam_editor_cpu']:
device = 'cpu'
else:
device = comfy.model_management.get_torch_device()
sam_predictor.model.to(device=device)
try:
data = await request.json()
positive_points = data['positive_points']
negative_points = data['negative_points']
threshold = data['threshold']
points = []
plabs = []
for p in positive_points:
points.append(p)
plabs.append(1)
for p in negative_points:
points.append(p)
plabs.append(0)
detected_masks = core.sam_predict(sam_predictor, points, plabs, None, threshold)
mask = utils.combine_masks2(detected_masks)
if mask is None:
return web.Response(status=400)
image = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
i = 255. * image.cpu().numpy()
img = Image.fromarray(np.clip(i[0], 0, 255).astype(np.uint8))
img_buffer = io.BytesIO()
img.save(img_buffer, format='png')
headers = {'Content-Type': 'image/png'}
finally:
sam_predictor.model.to(device="cpu")
return web.Response(body=img_buffer.getvalue(), headers=headers)
else:
return web.Response(status=400)
@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)
@PromptServer.instance.routes.get("/impact/wildcards/list/loaded")
async def wildcards_list_loaded(request):
"""
Get list of actually loaded wildcards (progressive loading in on-demand mode).
Returns:
- In on-demand mode: only wildcards that have been loaded into memory
- In full cache mode: same as /wildcards/list (all wildcards)
"""
data = {
'data': impact.wildcards.get_loaded_wildcard_list(),
'on_demand_mode': impact.wildcards.is_on_demand_mode(),
'total_available': len(impact.wildcards.available_wildcards) if impact.wildcards.is_on_demand_mode() else len(impact.wildcards.wildcard_dict)
}
return web.json_response(data)
@PromptServer.instance.routes.post("/impact/wildcards")
async def populate_wildcards(request):
data = await request.json()
populated = impact.wildcards.process(data['text'], data.get('seed', None))
return web.json_response({"text": populated})
segs_picker_map = {}
@PromptServer.instance.routes.get("/impact/segs/picker/count")
async def segs_picker_count(request):
node_id = request.rel_url.query.get('id', '')
if node_id in segs_picker_map:
res = len(segs_picker_map[node_id])
return web.Response(status=200, text=str(res))
return web.Response(status=400)
@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', ''))
if node_id in segs_picker_map and idx < len(segs_picker_map[node_id]):
img = to_tensor(segs_picker_map[node_id][idx]).permute(0, 3, 1, 2).squeeze(0)
pil = torchvision.transforms.ToPILImage('RGB')(img)
image_bytes = BytesIO()
pil.save(image_bytes, format="PNG")
image_bytes.seek(0)
return web.Response(status=200, body=image_bytes, content_type='image/png', headers={"Content-Disposition": f"filename={node_id}{idx}.png"})
return web.Response(status=400)
@PromptServer.instance.routes.get("/view/validate")
async def view_validate(request):
if "filename" in request.rel_url.query:
filename = request.rel_url.query["filename"]
subfolder = request.rel_url.query["subfolder"]
filename, base_dir = folder_paths.annotated_filepath(filename)
if filename == '' or filename[0] == '/' or '..' in filename:
return web.Response(status=400)
if base_dir is None:
base_dir = folder_paths.get_input_directory()
file = os.path.join(base_dir, subfolder, filename)
if os.path.isfile(file):
return web.Response(status=200)
return web.Response(status=400)
@PromptServer.instance.routes.get("/impact/validate/pb_id_image")
async def view_pb_id_image(request):
if "id" in request.rel_url.query:
pb_id = request.rel_url.query["id"]
if pb_id not in core.preview_bridge_image_id_map:
return web.Response(status=400)
file = core.preview_bridge_image_id_map[pb_id]
if os.path.isfile(file):
return web.Response(status=200)
return web.Response(status=400)
@PromptServer.instance.routes.get("/impact/set/pb_id_image")
async def set_previewbridge_image(request):
try:
if "filename" in request.rel_url.query:
node_id = request.rel_url.query["node_id"]
filename = request.rel_url.query["filename"]
path_type = request.rel_url.query["type"]
subfolder = request.rel_url.query["subfolder"]
filename, output_dir = folder_paths.annotated_filepath(filename)
if filename == '' or filename[0] == '/' or '..' in filename:
return web.Response(status=400)
if output_dir is None:
if path_type == 'input':
output_dir = folder_paths.get_input_directory()
elif path_type == 'output':
output_dir = folder_paths.get_output_directory()
else:
output_dir = folder_paths.get_temp_directory()
file = os.path.join(output_dir, subfolder, filename)
item = {
'filename': filename,
'type': path_type,
'subfolder': subfolder,
}
pb_id = core.set_previewbridge_image(node_id, file, item)
return web.Response(status=200, text=pb_id)
except Exception:
traceback.print_exc()
return web.Response(status=400)
@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"]
if pb_id in core.preview_bridge_image_id_map:
_, path_item = core.preview_bridge_image_id_map[pb_id]
return web.json_response(path_item)
return web.Response(status=400)
@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"]
if pb_id in core.preview_bridge_image_id_map:
file = core.preview_bridge_image_id_map[pb_id]
with Image.open(file):
filename = os.path.basename(file)
return web.FileResponse(file, headers={"Content-Disposition": f"filename=\"{filename}\""})
return web.Response(status=400)
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:
continue
cls = v['class_type']
if cls == 'ImpactInversedSwitch':
# if 'sel_mode' is 'select_on_prompt'
if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'select' in v['inputs']:
select_input = v['inputs']['select']
# if 'select' is converted input
if isinstance(select_input, list) and len(select_input) == 2:
input_node = json_data['prompt'][select_input[0]]
if input_node['class_type'] == 'ImpactInt' and 'inputs' in input_node and 'value' in input_node['inputs']:
inversed_switch_info[k] = input_node['inputs']['value']
else:
logging.warning(f"\n##### ##### #####\n[Impact Pack] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactInversedSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs if 'select_on_prompt' is selected.\n##### ##### #####\n")
else:
inversed_switch_info[k] = select_input
elif cls in ['ImpactSwitch', 'LatentSwitch', 'SEGSSwitch', 'ImpactMakeImageList']:
# if 'sel_mode' is 'select_on_prompt'
if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'select' in v['inputs']:
select_input = v['inputs']['select']
# if 'select' is converted input
if isinstance(select_input, list) and len(select_input) == 2:
input_node = json_data['prompt'][select_input[0]]
if input_node['class_type'] == 'ImpactInt' and 'inputs' in input_node and 'value' in input_node['inputs']:
onprompt_switch_info[k] = input_node['inputs']['value']
if input_node['class_type'] == 'ImpactSwitch' and 'inputs' in input_node and 'select' in input_node['inputs']:
if isinstance(input_node['inputs']['select'], int):
onprompt_switch_info[k] = input_node['inputs']['select']
else:
logging.warning(f"\n##### ##### #####\n[Impact Pack] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs if 'select_on_prompt' is selected.\n##### ##### #####\n")
else:
onprompt_switch_info[k] = select_input
if k in onprompt_switch_info and f'input{onprompt_switch_info[k]}' not in v['inputs']:
# disconnect output
disabled_switch.add(k)
elif cls == 'ImpactConditionalBranchSelMode':
if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'cond' in v['inputs']:
cond_input = v['inputs']['cond']
if isinstance(cond_input, list) and len(cond_input) == 2:
input_node = json_data['prompt'][cond_input[0]]
if (input_node['class_type'] == 'ImpactValueReceiver' and 'inputs' in input_node
and 'value' in input_node['inputs'] and 'typ' in input_node['inputs']):
if 'BOOLEAN' == input_node['inputs']['typ']:
try:
onprompt_cond_branch_info[k] = input_node['inputs']['value'].lower() == "true"
except Exception:
pass
else:
onprompt_cond_branch_info[k] = cond_input
for k, v in json_data['prompt'].items():
disable_targets = set()
for kk, vv in v['inputs'].items():
if isinstance(vv, list) and len(vv) == 2:
if vv[0] in inversed_switch_info:
if vv[1] + 1 != inversed_switch_info[vv[0]]:
disable_targets.add(kk)
else:
del inversed_switch_info[k]
if vv[0] in disabled_switch:
disable_targets.add(kk)
if k in onprompt_switch_info:
selected_slot_name = f"input{onprompt_switch_info[k]}"
for kk, vv in v['inputs'].items():
if kk != selected_slot_name and kk.startswith('input'):
disable_targets.add(kk)
if k in onprompt_cond_branch_info:
selected_slot_name = "tt_value" if onprompt_cond_branch_info[k] else "ff_value"
for kk, vv in v['inputs'].items():
if kk in ['tt_value', 'ff_value'] and kk != selected_slot_name:
disable_targets.add(kk)
for kk in disable_targets:
del v['inputs'][kk]
# inversed_switch - select out of range
for target in inversed_switch_info.keys():
del json_data['prompt'][target]['inputs']['input']
def onprompt_for_pickers(json_data):
detected_pickers = set()
for k, v in json_data['prompt'].items():
if 'class_type' not in v:
continue
cls = v['class_type']
if cls == 'ImpactSEGSPicker':
detected_pickers.add(k)
# garbage collection
keys_to_remove = [key for key in segs_picker_map if key not in detected_pickers]
for key in keys_to_remove:
del segs_picker_map[key]
def gc_preview_bridge_cache(json_data):
prompt_keys = json_data['prompt'].keys()
for key in list(core.preview_bridge_cache.keys()):
if key not in prompt_keys:
# 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']
for v in prompt.values():
if 'class_type' in v and v['class_type'] == 'ImageReceiver':
if v['inputs']['save_to_workflow']:
v['inputs']['image'] = "#DATA"
def regional_sampler_seed_update(json_data):
prompt = json_data['prompt']
for k, v in prompt.items():
if 'class_type' in v and v['class_type'] == 'RegionalSampler':
seed_2nd_mode = v['inputs']['seed_2nd_mode']
new_seed = None
if seed_2nd_mode == 'increment':
new_seed = v['inputs']['seed_2nd']+1
if new_seed > 1125899906842624:
new_seed = 0
elif seed_2nd_mode == 'decrement':
new_seed = v['inputs']['seed_2nd']-1
if new_seed < 0:
new_seed = 1125899906842624
elif seed_2nd_mode == 'randomize':
new_seed = random.randint(0, 1125899906842624)
if new_seed is not None:
PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "seed_2nd", "type": "INT", "value": new_seed})
def find_input_value(input_node, prompt, input_type=int, input_keys=('value',)):
input_val = None
try:
for n in input_keys:
input_val = input_node['inputs'].get(n, None)
if isinstance(input_val, input_type):
break
elif isinstance(input_val, list) and len(input_val):
input_val = find_input_value(prompt[input_val[0]], prompt=prompt, input_type=input_type, input_keys=input_keys)
if input_val is not None:
break
except Exception as e :
logging.warning(f"[Impact Pack] Error encountered on find {input_type} value - {e}")
return input_val
def onprompt_populate_wildcards(json_data):
prompt = json_data['prompt']
updated_widget_values = {}
for k, v in prompt.items():
if 'class_type' in v and (v['class_type'] == 'ImpactWildcardEncode' or v['class_type'] == 'ImpactWildcardProcessor'):
inputs = v['inputs']
# legacy adapter
if isinstance(inputs['mode'], bool):
if inputs['mode']:
new_mode = 'populate'
else:
new_mode = 'fixed'
inputs['mode'] = new_mode
if inputs['mode'] == 'populate' and isinstance(inputs['populated_text'], str):
if isinstance(inputs['seed'], list):
try:
input_node = prompt[inputs['seed'][0]]
if input_node['class_type'] == 'ImpactInt':
input_seed = int(input_node['inputs']['value'])
if not isinstance(input_seed, int):
continue
elif input_node['class_type'] == 'Seed (rgthree)':
input_seed = int(input_node['inputs']['seed'])
if not isinstance(input_seed, int):
continue
else:
input_seed = find_input_value(input_node, prompt=prompt, input_type=int, input_keys=('int', 'seed', 'value'))
if input_seed is None:
logging.info(f"[Impact Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ")
continue
except Exception:
continue
else:
input_seed = int(inputs['seed'])
inputs['populated_text'] = impact.wildcards.process(inputs['wildcard_text'], input_seed)
inputs['mode'] = 'reproduce'
PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "populated_text", "type": "STRING", "value": inputs['populated_text']})
updated_widget_values[k] = inputs['populated_text']
if inputs['mode'] == 'reproduce':
PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "mode", "type": "STRING", "value": 'populate'})
match json_data:
case {"extra_data": {"extra_pnginfo": {"workflow": {"nodes": nodes}}}}:
for node in nodes:
match node:
case {"id": id, "widgets_values": widgets_values}:
key = str(id)
if key in updated_widget_values:
widgets_values[1] = updated_widget_values[key]
widgets_values[2] = "reproduce"
def onprompt_for_remote(json_data):
prompt = json_data['prompt']
for v in prompt.values():
if 'class_type' in v:
cls = v['class_type']
if cls == 'ImpactRemoteBoolean' or cls == 'ImpactRemoteInt':
inputs = v['inputs']
node_id = str(inputs['node_id'])
if node_id not in prompt:
continue
target_inputs = prompt[node_id]['inputs']
widget_name = inputs['widget_name']
if widget_name in target_inputs:
widget_type = None
if cls == 'ImpactRemoteBoolean' and isinstance(target_inputs[widget_name], bool):
widget_type = 'BOOLEAN'
elif cls == 'ImpactRemoteInt' and (isinstance(target_inputs[widget_name], int) or isinstance(target_inputs[widget_name], float)):
widget_type = 'INT'
if widget_type is None:
break
target_inputs[widget_name] = 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):
try:
onprompt_for_remote(json_data) # NOTE: top priority
onprompt_for_switch(json_data)
onprompt_for_pickers(json_data)
onprompt_populate_wildcards(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:
logging.exception("[Impact Pack] ComfyUI-Impact-Pack: Error on prompt - several features will not work.")
return json_data
PromptServer.instance.add_on_prompt_handler(onprompt)

779
custom_nodes/ComfyUI-Impact-Pack/modules/impact/logics.py Normal file
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@@ -0,0 +1,779 @@
import sys
import time
import execution
import impact.impact_server
from server import PromptServer
from impact.utils import any_typ
import impact.core as core
import re
import nodes
import logging
class ImpactCompare:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"cmp": (['a = b', 'a <> b', 'a > b', 'a < b', 'a >= b', 'a <= b', 'tt', 'ff'],),
"a": (any_typ, ),
"b": (any_typ, ),
},
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
RETURN_TYPES = ("BOOLEAN", )
def doit(self, cmp, a, b):
if cmp == "a = b":
return (a == b, )
elif cmp == "a <> b":
return (a != b, )
elif cmp == "a > b":
return (a > b, )
elif cmp == "a < b":
return (a < b, )
elif cmp == "a >= b":
return (a >= b, )
elif cmp == "a <= b":
return (a <= b, )
elif cmp == 'tt':
return (True, )
else:
return (False, )
class ImpactNotEmptySEGS:
@classmethod
def INPUT_TYPES(cls):
return {"required": {"segs": ("SEGS",)}}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
RETURN_TYPES = ("BOOLEAN", )
def doit(self, segs):
return (segs[1] != [], )
class ImpactConditionalBranch:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"cond": ("BOOLEAN",),
"tt_value": (any_typ,{"lazy": True}),
"ff_value": (any_typ,{"lazy": True}),
},
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
RETURN_TYPES = (any_typ, )
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:
return (ff_value,)
class ImpactConditionalBranchSelMode:
@classmethod
def INPUT_TYPES(cls):
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,),
},
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
RETURN_TYPES = (any_typ, )
def doit(self, cond, tt_value=None, ff_value=None, **kwargs):
if cond:
return (tt_value,)
else:
return (ff_value,)
class ImpactConvertDataType:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {"required": {"value": (any_typ,)}}
RETURN_TYPES = ("STRING", "FLOAT", "INT", "BOOLEAN")
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
@staticmethod
def is_number(string):
pattern = re.compile(r'^[-+]?[0-9]*\.?[0-9]+$')
return bool(pattern.match(string))
def doit(self, value):
if self.is_number(str(value)):
num = value
else:
if str.lower(str(value)) != "false":
num = 1
else:
num = 0
return (str(value), float(num), int(float(num)), bool(float(num)), )
class ImpactIfNone:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {},
"optional": {"signal": (any_typ,), "any_input": (any_typ,), }
}
RETURN_TYPES = (any_typ, "BOOLEAN")
RETURN_NAMES = ("signal_opt", "bool")
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
def doit(self, signal=None, any_input=None):
if any_input is None:
return (signal, False, )
else:
return (signal, True, )
class ImpactLogicalOperators:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"operator": (['and', 'or', 'xor'],),
"bool_a": ("BOOLEAN", {"forceInput": True}),
"bool_b": ("BOOLEAN", {"forceInput": True}),
},
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
RETURN_TYPES = ("BOOLEAN", )
def doit(self, operator, bool_a, bool_b):
if operator == "and":
return (bool_a and bool_b, )
elif operator == "or":
return (bool_a or bool_b, )
else:
return (bool_a != bool_b, )
class ImpactConditionalStopIteration:
@classmethod
def INPUT_TYPES(cls):
return {
"required": { "cond": ("BOOLEAN", {"forceInput": True}), },
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
RETURN_TYPES = ()
OUTPUT_NODE = True
def doit(self, cond):
if cond:
PromptServer.instance.send_sync("stop-iteration", {})
return {}
class ImpactNeg:
@classmethod
def INPUT_TYPES(cls):
return {
"required": { "value": ("BOOLEAN", {"forceInput": True}), },
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
RETURN_TYPES = ("BOOLEAN", )
def doit(self, value):
return (not value, )
class ImpactInt:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"value": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
},
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
RETURN_TYPES = ("INT", )
def doit(self, value):
return (value, )
class ImpactFloat:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"value": ("FLOAT", {"default": 1.0, "min": -3.402823466e+38, "max": 3.402823466e+38}),
},
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
RETURN_TYPES = ("FLOAT", )
def doit(self, value):
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):
return {"required": {
"value": (any_typ, ),
"link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
},
"optional": {
"signal_opt": (any_typ,),
}
}
OUTPUT_NODE = True
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
RETURN_TYPES = (any_typ, )
RETURN_NAMES = ("signal", )
def doit(self, value, link_id=0, signal_opt=None):
PromptServer.instance.send_sync("value-send", {"link_id": link_id, "value": value})
return (signal_opt, )
class ImpactIntConstSender:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"signal": (any_typ, ),
"value": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
"link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
},
}
OUTPUT_NODE = True
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
RETURN_TYPES = ()
def doit(self, signal, value, link_id=0):
PromptServer.instance.send_sync("value-send", {"link_id": link_id, "value": value})
return {}
class ImpactValueReceiver:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"typ": (["STRING", "INT", "FLOAT", "BOOLEAN"], ),
"value": ("STRING", {"default": ""}),
"link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
},
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic"
RETURN_TYPES = (any_typ, )
def doit(self, typ, value, link_id=0):
if typ == "INT":
return (int(value), )
elif typ == "FLOAT":
return (float(value), )
elif typ == "BOOLEAN":
return (value.lower() == "true", )
else:
return (value, )
class ImpactImageInfo:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"value": ("IMAGE", ),
},
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic/_for_test"
RETURN_TYPES = ("INT", "INT", "INT", "INT")
RETURN_NAMES = ("batch", "height", "width", "channel")
def doit(self, value):
return (value.shape[0], value.shape[1], value.shape[2], value.shape[3])
class ImpactLatentInfo:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"value": ("LATENT", ),
},
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic/_for_test"
RETURN_TYPES = ("INT", "INT", "INT", "INT")
RETURN_NAMES = ("batch", "height", "width", "channel")
def doit(self, value):
shape = value['samples'].shape
return (shape[0], shape[2] * 8, shape[3] * 8, shape[1])
class ImpactMinMax:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"mode": ("BOOLEAN", {"default": True, "label_on": "max", "label_off": "min"}),
"a": (any_typ,),
"b": (any_typ,),
},
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic/_for_test"
RETURN_TYPES = ("INT", )
def doit(self, mode, a, b):
if mode:
return (max(a, b), )
else:
return (min(a, b),)
class ImpactQueueTrigger:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"signal": (any_typ,),
"mode": ("BOOLEAN", {"default": True, "label_on": "Trigger", "label_off": "Don't trigger"}),
}
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic/_for_test"
RETURN_TYPES = (any_typ,)
RETURN_NAMES = ("signal_opt",)
OUTPUT_NODE = True
def doit(self, signal, mode):
if(mode):
PromptServer.instance.send_sync("impact-add-queue", {})
return (signal,)
class ImpactQueueTriggerCountdown:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"count": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"total": ("INT", {"default": 10, "min": 1, "max": 0xffffffffffffffff}),
"mode": ("BOOLEAN", {"default": True, "label_on": "Trigger", "label_off": "Don't trigger"}),
},
"optional": {"signal": (any_typ,),},
"hidden": {"unique_id": "UNIQUE_ID"}
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic/_for_test"
RETURN_TYPES = (any_typ, "INT", "INT")
RETURN_NAMES = ("signal_opt", "count", "total")
OUTPUT_NODE = True
def doit(self, count, total, mode, unique_id, signal=None):
if (mode):
if count < total - 1:
PromptServer.instance.send_sync("impact-node-feedback",
{"node_id": unique_id, "widget_name": "count", "type": "int", "value": count+1})
PromptServer.instance.send_sync("impact-add-queue", {})
if count >= total - 1:
PromptServer.instance.send_sync("impact-node-feedback",
{"node_id": unique_id, "widget_name": "count", "type": "int", "value": 0})
return (signal, count, total)
class ImpactSetWidgetValue:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"signal": (any_typ,),
"node_id": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"widget_name": ("STRING", {"multiline": False}),
},
"optional": {
"boolean_value": ("BOOLEAN", {"forceInput": True}),
"int_value": ("INT", {"forceInput": True}),
"float_value": ("FLOAT", {"forceInput": True}),
"string_value": ("STRING", {"forceInput": True}),
}
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic/_for_test"
RETURN_TYPES = (any_typ,)
RETURN_NAMES = ("signal_opt",)
OUTPUT_NODE = True
def doit(self, signal, node_id, widget_name, boolean_value=None, int_value=None, float_value=None, string_value=None, ):
kind = None
if boolean_value is not None:
value = boolean_value
kind = "BOOLEAN"
elif int_value is not None:
value = int_value
kind = "INT"
elif float_value is not None:
value = float_value
kind = "FLOAT"
elif string_value is not None:
value = string_value
kind = "STRING"
else:
value = None
if value is not None:
PromptServer.instance.send_sync("impact-node-feedback",
{"node_id": node_id, "widget_name": widget_name, "type": kind, "value": value})
return (signal,)
class ImpactNodeSetMuteState:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"signal": (any_typ,),
"node_id": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"set_state": ("BOOLEAN", {"default": True, "label_on": "active", "label_off": "mute"}),
}
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic/_for_test"
RETURN_TYPES = (any_typ,)
RETURN_NAMES = ("signal_opt",)
OUTPUT_NODE = True
def doit(self, signal, node_id, set_state):
PromptServer.instance.send_sync("impact-node-mute-state", {"node_id": node_id, "is_active": set_state})
return (signal,)
class ImpactSleep:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"signal": (any_typ,),
"seconds": ("FLOAT", {"default": 0.5, "min": 0, "max": 3600}),
}
}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic/_for_test"
RETURN_TYPES = (any_typ,)
RETURN_NAMES = ("signal_opt",)
OUTPUT_NODE = True
def doit(self, signal, seconds):
time.sleep(seconds)
return (signal,)
def workflow_to_map(workflow):
nodes = {}
links = {}
for link in workflow['links']:
links[link[0]] = link[1:]
for node in workflow['nodes']:
nodes[str(node['id'])] = node
return nodes, links
class ImpactRemoteBoolean:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"node_id": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"widget_name": ("STRING", {"multiline": False}),
"value": ("BOOLEAN", {"default": True, "label_on": "True", "label_off": "False"}),
}}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic/_for_test"
RETURN_TYPES = ()
OUTPUT_NODE = True
def doit(self, **kwargs):
return {}
class ImpactRemoteInt:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"node_id": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"widget_name": ("STRING", {"multiline": False}),
"value": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff}),
}}
FUNCTION = "doit"
CATEGORY = "ImpactPack/Logic/_for_test"
RETURN_TYPES = ()
OUTPUT_NODE = True
def doit(self, **kwargs):
return {}
class ImpactControlBridge:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"value": (any_typ,),
"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"
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="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 Exception:
logging.info("[Impact Pack] core.current_prompt['extra_data']['extra_pnginfo']['workflow']")
return 0
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)
return next_nodes
def doit(self, value, mode, behavior="Stop", unique_id=None, prompt=None, extra_pnginfo=None):
global error_skip_flag
if core.is_execution_model_version_supported():
from comfy_execution.graph import ExecutionBlocker
else:
logging.info("[Impact Pack] ImpactControlBridge: ComfyUI is outdated. The 'Stop' behavior cannot function properly.")
if behavior == "Stop":
if mode:
return (value, )
else:
return (ExecutionBlocker(None), )
elif extra_pnginfo is None:
logging.warning(f"[Impact Pack] limitation: '{behavior}' behavior cannot be used in API execution.")
return (value,)
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: # noqa: E712
# 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):
execution.PromptExecutor.handle_execution_error(**kwargs)

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custom_nodes/ComfyUI-Impact-Pack/modules/impact/pipe.py Normal file
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import folder_paths
from impact.utils import any_typ
class ToDetailerPipe:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"clip": ("CLIP",),
"vae": ("VAE",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"bbox_detector": ("BBOX_DETECTOR", ),
"wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
"Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
"Select to add Wildcard": (["Select the Wildcard to add to the text"], ),
},
"optional": {
"sam_model_opt": ("SAM_MODEL",),
"segm_detector_opt": ("SEGM_DETECTOR",),
"detailer_hook": ("DETAILER_HOOK",),
}}
RETURN_TYPES = ("DETAILER_PIPE", )
RETURN_NAMES = ("detailer_pipe", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Pipe"
def doit(self, *args, **kwargs):
pipe = (kwargs['model'], kwargs['clip'], kwargs['vae'], kwargs['positive'], kwargs['negative'], kwargs['wildcard'], kwargs['bbox_detector'],
kwargs.get('segm_detector_opt', None), kwargs.get('sam_model_opt', None), kwargs.get('detailer_hook', None),
kwargs.get('refiner_model', None), kwargs.get('refiner_clip', None),
kwargs.get('refiner_positive', None), kwargs.get('refiner_negative', None))
return (pipe, )
class ToDetailerPipeSDXL(ToDetailerPipe):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"clip": ("CLIP",),
"vae": ("VAE",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"refiner_model": ("MODEL",),
"refiner_clip": ("CLIP",),
"refiner_positive": ("CONDITIONING",),
"refiner_negative": ("CONDITIONING",),
"bbox_detector": ("BBOX_DETECTOR", ),
"wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
"Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
"Select to add Wildcard": (["Select the Wildcard to add to the text"],),
},
"optional": {
"sam_model_opt": ("SAM_MODEL",),
"segm_detector_opt": ("SEGM_DETECTOR",),
"detailer_hook": ("DETAILER_HOOK",),
}}
class FromDetailerPipe:
@classmethod
def INPUT_TYPES(s):
return {"required": {"detailer_pipe": ("DETAILER_PIPE",), }, }
RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING", "BBOX_DETECTOR", "SAM_MODEL", "SEGM_DETECTOR", "DETAILER_HOOK")
RETURN_NAMES = ("model", "clip", "vae", "positive", "negative", "bbox_detector", "sam_model_opt", "segm_detector_opt", "detailer_hook")
FUNCTION = "doit"
CATEGORY = "ImpactPack/Pipe"
def doit(self, detailer_pipe):
model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, _, _, _, _ = detailer_pipe
return model, clip, vae, positive, negative, bbox_detector, sam_model_opt, segm_detector_opt, detailer_hook
class FromDetailerPipe_v2:
@classmethod
def INPUT_TYPES(s):
return {"required": {"detailer_pipe": ("DETAILER_PIPE",), }, }
RETURN_TYPES = ("DETAILER_PIPE", "MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING", "BBOX_DETECTOR", "SAM_MODEL", "SEGM_DETECTOR", "DETAILER_HOOK")
RETURN_NAMES = ("detailer_pipe", "model", "clip", "vae", "positive", "negative", "bbox_detector", "sam_model_opt", "segm_detector_opt", "detailer_hook")
FUNCTION = "doit"
CATEGORY = "ImpactPack/Pipe"
def doit(self, detailer_pipe):
model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, _, _, _, _ = detailer_pipe
return detailer_pipe, model, clip, vae, positive, negative, bbox_detector, sam_model_opt, segm_detector_opt, detailer_hook
class FromDetailerPipe_SDXL:
@classmethod
def INPUT_TYPES(s):
return {"required": {"detailer_pipe": ("DETAILER_PIPE",), }, }
RETURN_TYPES = ("DETAILER_PIPE", "MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING", "BBOX_DETECTOR", "SAM_MODEL", "SEGM_DETECTOR", "DETAILER_HOOK", "MODEL", "CLIP", "CONDITIONING", "CONDITIONING")
RETURN_NAMES = ("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")
FUNCTION = "doit"
CATEGORY = "ImpactPack/Pipe"
def doit(self, detailer_pipe):
model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, refiner_model, refiner_clip, refiner_positive, refiner_negative = detailer_pipe
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):
return {"required": {
"model": ("MODEL",),
"clip": ("CLIP",),
"vae": ("VAE",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
},
}
RETURN_TYPES = ("BASIC_PIPE", )
RETURN_NAMES = ("basic_pipe", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Pipe"
def doit(self, model, clip, vae, positive, negative):
pipe = (model, clip, vae, positive, negative)
return (pipe, )
class FromBasicPipe:
@classmethod
def INPUT_TYPES(s):
return {"required": {"basic_pipe": ("BASIC_PIPE",), }, }
RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING")
RETURN_NAMES = ("model", "clip", "vae", "positive", "negative")
FUNCTION = "doit"
CATEGORY = "ImpactPack/Pipe"
def doit(self, basic_pipe):
model, clip, vae, positive, negative = basic_pipe
return model, clip, vae, positive, negative
class FromBasicPipe_v2:
@classmethod
def INPUT_TYPES(s):
return {"required": {"basic_pipe": ("BASIC_PIPE",), }, }
RETURN_TYPES = ("BASIC_PIPE", "MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING")
RETURN_NAMES = ("basic_pipe", "model", "clip", "vae", "positive", "negative")
FUNCTION = "doit"
CATEGORY = "ImpactPack/Pipe"
def doit(self, basic_pipe):
model, clip, vae, positive, negative = basic_pipe
return basic_pipe, model, clip, vae, positive, negative
class BasicPipeToDetailerPipe:
@classmethod
def INPUT_TYPES(s):
return {"required": {"basic_pipe": ("BASIC_PIPE",),
"bbox_detector": ("BBOX_DETECTOR", ),
"wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
"Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
"Select to add Wildcard": (["Select the Wildcard to add to the text"],),
},
"optional": {
"sam_model_opt": ("SAM_MODEL", ),
"segm_detector_opt": ("SEGM_DETECTOR",),
"detailer_hook": ("DETAILER_HOOK",),
},
}
RETURN_TYPES = ("DETAILER_PIPE", )
RETURN_NAMES = ("detailer_pipe", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Pipe"
def doit(self, *args, **kwargs):
basic_pipe = kwargs['basic_pipe']
bbox_detector = kwargs['bbox_detector']
wildcard = kwargs['wildcard']
sam_model_opt = kwargs.get('sam_model_opt', None)
segm_detector_opt = kwargs.get('segm_detector_opt', None)
detailer_hook = kwargs.get('detailer_hook', None)
model, clip, vae, positive, negative = basic_pipe
pipe = model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, None, None, None, None
return (pipe, )
class BasicPipeToDetailerPipeSDXL:
@classmethod
def INPUT_TYPES(s):
return {"required": {"base_basic_pipe": ("BASIC_PIPE",),
"refiner_basic_pipe": ("BASIC_PIPE",),
"bbox_detector": ("BBOX_DETECTOR", ),
"wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
"Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
"Select to add Wildcard": (["Select the Wildcard to add to the text"],),
},
"optional": {
"sam_model_opt": ("SAM_MODEL", ),
"segm_detector_opt": ("SEGM_DETECTOR",),
"detailer_hook": ("DETAILER_HOOK",),
},
}
RETURN_TYPES = ("DETAILER_PIPE", )
RETURN_NAMES = ("detailer_pipe", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Pipe"
def doit(self, *args, **kwargs):
base_basic_pipe = kwargs['base_basic_pipe']
refiner_basic_pipe = kwargs['refiner_basic_pipe']
bbox_detector = kwargs['bbox_detector']
wildcard = kwargs['wildcard']
sam_model_opt = kwargs.get('sam_model_opt', None)
segm_detector_opt = kwargs.get('segm_detector_opt', None)
detailer_hook = kwargs.get('detailer_hook', None)
model, clip, vae, positive, negative = base_basic_pipe
refiner_model, refiner_clip, refiner_vae, refiner_positive, refiner_negative = refiner_basic_pipe
pipe = model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, refiner_model, refiner_clip, refiner_positive, refiner_negative
return (pipe, )
class DetailerPipeToBasicPipe:
@classmethod
def INPUT_TYPES(s):
return {"required": {"detailer_pipe": ("DETAILER_PIPE",), }}
RETURN_TYPES = ("BASIC_PIPE", "BASIC_PIPE")
RETURN_NAMES = ("base_basic_pipe", "refiner_basic_pipe")
FUNCTION = "doit"
CATEGORY = "ImpactPack/Pipe"
def doit(self, detailer_pipe):
model, clip, vae, positive, negative, _, _, _, _, _, refiner_model, refiner_clip, refiner_positive, refiner_negative = detailer_pipe
pipe = model, clip, vae, positive, negative
refiner_pipe = refiner_model, refiner_clip, vae, refiner_positive, refiner_negative
return (pipe, refiner_pipe)
class EditBasicPipe:
@classmethod
def INPUT_TYPES(s):
return {
"required": {"basic_pipe": ("BASIC_PIPE",), },
"optional": {
"model": ("MODEL",),
"clip": ("CLIP",),
"vae": ("VAE",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
},
}
RETURN_TYPES = ("BASIC_PIPE", )
RETURN_NAMES = ("basic_pipe", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Pipe"
def doit(self, basic_pipe, model=None, clip=None, vae=None, positive=None, negative=None):
res_model, res_clip, res_vae, res_positive, res_negative = basic_pipe
if model is not None:
res_model = model
if clip is not None:
res_clip = clip
if vae is not None:
res_vae = vae
if positive is not None:
res_positive = positive
if negative is not None:
res_negative = negative
pipe = res_model, res_clip, res_vae, res_positive, res_negative
return (pipe, )
class EditDetailerPipe:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"detailer_pipe": ("DETAILER_PIPE",),
"wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
"Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
"Select to add Wildcard": (["Select the Wildcard to add to the text"],),
},
"optional": {
"model": ("MODEL",),
"clip": ("CLIP",),
"vae": ("VAE",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"bbox_detector": ("BBOX_DETECTOR",),
"sam_model": ("SAM_MODEL",),
"segm_detector": ("SEGM_DETECTOR",),
"detailer_hook": ("DETAILER_HOOK",),
},
}
RETURN_TYPES = ("DETAILER_PIPE",)
RETURN_NAMES = ("detailer_pipe",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Pipe"
def doit(self, *args, **kwargs):
detailer_pipe = kwargs['detailer_pipe']
wildcard = kwargs['wildcard']
model = kwargs.get('model', None)
clip = kwargs.get('clip', None)
vae = kwargs.get('vae', None)
positive = kwargs.get('positive', None)
negative = kwargs.get('negative', None)
bbox_detector = kwargs.get('bbox_detector', None)
sam_model = kwargs.get('sam_model', None)
segm_detector = kwargs.get('segm_detector', None)
detailer_hook = kwargs.get('detailer_hook', None)
refiner_model = kwargs.get('refiner_model', None)
refiner_clip = kwargs.get('refiner_clip', None)
refiner_positive = kwargs.get('refiner_positive', None)
refiner_negative = kwargs.get('refiner_negative', None)
res_model, res_clip, res_vae, res_positive, res_negative, res_wildcard, res_bbox_detector, res_segm_detector, res_sam_model, res_detailer_hook, res_refiner_model, res_refiner_clip, res_refiner_positive, res_refiner_negative = detailer_pipe
if model is not None:
res_model = model
if clip is not None:
res_clip = clip
if vae is not None:
res_vae = vae
if positive is not None:
res_positive = positive
if negative is not None:
res_negative = negative
if bbox_detector is not None:
res_bbox_detector = bbox_detector
if segm_detector is not None:
res_segm_detector = segm_detector
if wildcard != "":
res_wildcard = wildcard
if sam_model is not None:
res_sam_model = sam_model
if detailer_hook is not None:
res_detailer_hook = detailer_hook
if refiner_model is not None:
res_refiner_model = refiner_model
if refiner_clip is not None:
res_refiner_clip = refiner_clip
if refiner_positive is not None:
res_refiner_positive = refiner_positive
if refiner_negative is not None:
res_refiner_negative = refiner_negative
pipe = (res_model, res_clip, res_vae, res_positive, res_negative, res_wildcard,
res_bbox_detector, res_segm_detector, res_sam_model, res_detailer_hook,
res_refiner_model, res_refiner_clip, res_refiner_positive, res_refiner_negative)
return (pipe, )
class EditDetailerPipeSDXL(EditDetailerPipe):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"detailer_pipe": ("DETAILER_PIPE",),
"wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
"Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
"Select to add Wildcard": (["Select the Wildcard to add to the text"],),
},
"optional": {
"model": ("MODEL",),
"clip": ("CLIP",),
"vae": ("VAE",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"refiner_model": ("MODEL",),
"refiner_clip": ("CLIP",),
"refiner_positive": ("CONDITIONING",),
"refiner_negative": ("CONDITIONING",),
"bbox_detector": ("BBOX_DETECTOR",),
"sam_model": ("SAM_MODEL",),
"segm_detector": ("SEGM_DETECTOR",),
"detailer_hook": ("DETAILER_HOOK",),
},
}

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from impact import impact_sampling
from comfy import model_management
from impact import utils
from PIL import Image
import nodes
import torch
import inspect
import logging
import comfy
try:
from comfy_extras import nodes_differential_diffusion
except Exception:
logging.info("[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)
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 = utils.tensor2pil(image)
original_width, original_height = pil_img.size
scaled_image = utils.pil2tensor(apply_resize_image(utils.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 = utils.tensor_resize(image, w, h)
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().execute(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:
logging.info("[Impact Pack] ComfyUI is an outdated version.")
positive, negative, latent_image = imc_encode(positive, negative, image, vae, noise_mask)
else:
latent_image = utils.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 = utils.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

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import math
import impact.core as core
from comfy_extras.nodes_custom_sampler import Noise_RandomNoise
from nodes import MAX_RESOLUTION
import nodes
from impact.impact_sampling import KSamplerWrapper, KSamplerAdvancedWrapper, separated_sample, impact_sample
import comfy
import torch
import numpy as np
import logging
class TiledKSamplerProvider:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"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"
@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,
tile_width, tile_height, tiling_strategy)
return (sampler, )
class KSamplerProvider:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"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.get_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"
@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, scheduler_func=scheduler_func_opt)
return (sampler, )
class KSamplerAdvancedProvider:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"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.get_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", {"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"
@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, scheduler_func=scheduler_func_opt)
return (sampler, )
class TwoSamplersForMask:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"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"
@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
new_latent_image = base_sampler.sample(latent_image)
new_latent_image['noise_mask'] = mask
new_latent_image = mask_sampler.sample(new_latent_image)
del new_latent_image['noise_mask']
return (new_latent_image, )
class TwoAdvancedSamplersForMask:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"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 doit(seed, steps, denoise, samples, base_sampler, mask_sampler, mask, overlap_factor):
regional_prompts = RegionalPrompt().doit(mask=mask, advanced_sampler=mask_sampler)[0]
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", {"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"
@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], )
class CombineRegionalPrompts:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"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"
@staticmethod
def doit(**kwargs):
res = []
for k, v in kwargs.items():
res += v
return (res, )
class CombineConditionings:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"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"
@staticmethod
def doit(**kwargs):
res = []
for k, v in kwargs.items():
res += v
return (res, )
class ConcatConditionings:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"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"
@staticmethod
def doit(**kwargs):
conditioning_to = list(kwargs.values())[0]
for k, conditioning_from in list(kwargs.items())[1:]:
out = []
if len(conditioning_from) > 1:
logging.warning("Warning: ConcatConditionings {k} contains more than 1 cond, only the first one will actually be applied to conditioning1.")
cond_from = conditioning_from[0][0]
for i in range(len(conditioning_to)):
t1 = conditioning_to[i][0]
tw = torch.cat((t1, cond_from), 1)
n = [tw, conditioning_to[i][1].copy()]
out.append(n)
conditioning_to = out
return (out, )
class RegionalSampler:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"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"},
}
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()
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()
@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:
latent_compositor = None
masks = [regional_prompt.mask.numpy() for regional_prompt in regional_prompts]
masks = [np.ceil(mask).astype(np.int32) for mask in masks]
combined_mask = torch.from_numpy(np.bitwise_or.reduce(masks))
inv_mask = torch.where(combined_mask == 0, torch.tensor(1.0), torch.tensor(0.0))
adv_steps = int(steps / denoise)
start_at_step = adv_steps - steps
region_len = len(regional_prompts)
total = steps*region_len
leftover_noise = False
if base_only_steps > 0:
if seed_2nd_mode == 'ignore':
leftover_noise = True
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
if seed > 1125899906842624:
seed = seed - 1125899906842624
elif seed_2nd_mode == "seed-seed_2nd":
seed -= seed_2nd
if seed < 0:
seed += 1125899906842624
elif seed_2nd_mode != 'ignore':
seed = seed_2nd
new_latent_image = samples.copy()
base_latent_image = None
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,
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:
del new_latent_image['noise_mask']
base_latent_image = new_latent_image.copy()
j = 1
for regional_prompt in regional_prompts:
if restore_latent:
new_latent_image = base_latent_image.copy()
core.update_node_status(unique_id, f"{i}/{steps} steps | {j}/{region_len}", ((i-start_at_step)*region_len + j)/total)
region_mask = regional_prompt.get_mask_erosion(overlap_factor).squeeze(0).squeeze(0)
new_latent_image['noise_mask'] = region_mask
new_latent_image = regional_prompt.sampler.sample_advanced(False, seed, adv_steps, new_latent_image, i, i + 1, True,
recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
if restore_latent:
del new_latent_image['noise_mask']
base_latent_image = latent_compositor.composite(base_latent_image, new_latent_image, 0, 0, False, region_mask)[0]
new_latent_image = base_latent_image
j += 1
add_noise = False
# finalize
core.update_node_status(unique_id, "finalize")
if base_latent_image is not None:
new_latent_image = base_latent_image
else:
base_latent_image = new_latent_image
new_latent_image['noise_mask'] = inv_mask
new_latent_image = base_sampler.sample_advanced(False, seed, adv_steps, new_latent_image, adv_steps, adv_steps+1, False,
recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
core.update_node_status(unique_id, f"{steps}/{steps} steps", total)
core.update_node_status(unique_id, "", None)
if restore_latent:
new_latent_image = base_latent_image
if 'noise_mask' in new_latent_image:
del new_latent_image['noise_mask']
return (new_latent_image, )
class RegionalSamplerAdvanced:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"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"
@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:
latent_compositor = None
masks = [regional_prompt.mask.numpy() for regional_prompt in regional_prompts]
masks = [np.ceil(mask).astype(np.int32) for mask in masks]
combined_mask = torch.from_numpy(np.bitwise_or.reduce(masks))
inv_mask = torch.where(combined_mask == 0, torch.tensor(1.0), torch.tensor(0.0))
region_len = len(regional_prompts)
end_at_step = min(steps, end_at_step)
total = (end_at_step - start_at_step) * region_len
base_latent_image = None
region_masks = {}
for i in range(start_at_step, end_at_step-1):
core.update_node_status(unique_id, f"{start_at_step+i}/{end_at_step} steps | ", ((i-start_at_step)*region_len)/total)
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, noise=noise)
if restore_latent:
del new_latent_image['noise_mask']
base_latent_image = new_latent_image.copy()
j = 1
for regional_prompt in regional_prompts:
if restore_latent:
new_latent_image = base_latent_image.copy()
core.update_node_status(unique_id, f"{start_at_step+i}/{end_at_step} steps | {j}/{region_len}", ((i-start_at_step)*region_len + j)/total)
if j not in region_masks:
region_mask = regional_prompt.get_mask_erosion(overlap_factor).squeeze(0).squeeze(0)
region_masks[j] = region_mask
else:
region_mask = region_masks[j]
new_latent_image['noise_mask'] = region_mask
new_latent_image = regional_prompt.sampler.sample_advanced(False, noise_seed, steps, new_latent_image, i, i + 1, True,
recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
if restore_latent:
del new_latent_image['noise_mask']
base_latent_image = latent_compositor.composite(base_latent_image, new_latent_image, 0, 0, False, region_mask)[0]
new_latent_image = base_latent_image
j += 1
# finalize
core.update_node_status(unique_id, "finalize")
if base_latent_image is not None:
new_latent_image = base_latent_image
else:
base_latent_image = new_latent_image
new_latent_image['noise_mask'] = inv_mask
new_latent_image = base_sampler.sample_advanced(False, noise_seed, steps, new_latent_image, end_at_step-1, end_at_step, return_with_leftover_noise,
recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
core.update_node_status(unique_id, f"{end_at_step}/{end_at_step} steps", total)
core.update_node_status(unique_id, "", None)
if restore_latent:
new_latent_image = base_latent_image
if 'noise_mask' in new_latent_image:
del new_latent_image['noise_mask']
return (new_latent_image, )
class KSamplerBasicPipe:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"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.get_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 = "ImpactPack/sampling"
@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 = impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, scheduler_func=scheduler_func_opt)
return basic_pipe, latent, vae
class KSamplerAdvancedBasicPipe:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"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.get_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 = "ImpactPack/sampling"
@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
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", )

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custom_nodes/ComfyUI-Impact-Pack/modules/impact/util_nodes.py Normal file
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from impact.utils import any_typ, ByPassTypeTuple, make_3d_mask
import comfy_extras.nodes_mask
from nodes import MAX_RESOLUTION
import torch
import comfy
import sys
import nodes
import re
import impact.core as core
from server import PromptServer
import inspect
import logging
class GeneralSwitch:
@classmethod
def INPUT_TYPES(s):
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 check_lazy_status(self, *args, **kwargs):
selected_index = int(kwargs['select'])
input_name = f"input{selected_index}"
logging.info(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']
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']
for slot in inputs:
if slot['name'] == input_name and 'label' in slot:
selected_label = slot['label']
break
else:
logging.info("[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
else:
logging.info("ImpactSwitch: invalid select index (ignored)")
return None, "", selected_index
class LatentSwitch:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"select": ("INT", {"default": 1, "min": 1, "max": 99999, "step": 1}),
"latent1": ("LATENT",),
},
}
RETURN_TYPES = ("LATENT", )
OUTPUT_NODE = True
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, *args, **kwargs):
input_name = f"latent{int(kwargs['select'])}"
if input_name in kwargs:
return (kwargs[input_name],)
else:
logging.info("LatentSwitch: invalid select index ('latent1' is selected)")
return (kwargs['latent1'],)
class ImageMaskSwitch:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"select": ("INT", {"default": 1, "min": 1, "max": 4, "step": 1}),
"images1": ("IMAGE",),
},
"optional": {
"mask1_opt": ("MASK",),
"images2_opt": ("IMAGE",),
"mask2_opt": ("MASK",),
"images3_opt": ("IMAGE",),
"mask3_opt": ("MASK",),
"images4_opt": ("IMAGE",),
"mask4_opt": ("MASK",),
},
}
RETURN_TYPES = ("IMAGE", "MASK",)
OUTPUT_NODE = True
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, select, images1, mask1_opt=None, images2_opt=None, mask2_opt=None, images3_opt=None, mask3_opt=None,
images4_opt=None, mask4_opt=None):
if select == 1:
return images1, mask1_opt,
elif select == 2:
return images2_opt, mask2_opt,
elif select == 3:
return images3_opt, mask3_opt,
else:
return images4_opt, mask4_opt,
class GeneralInversedSwitch:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"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."}),
},
"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, prompt, unique_id, input, **kwargs):
if core.is_execution_model_version_supported():
from comfy_execution.graph import ExecutionBlocker
else:
logging.warning("[Impact Pack] InversedSwitch: ComfyUI is outdated. The 'select_on_execution' mode cannot function properly.")
res = []
# 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)
return res
class RemoveNoiseMask:
@classmethod
def INPUT_TYPES(s):
return {"required": {"samples": ("LATENT",)}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, samples):
res = {key: value for key, value in samples.items() if key != 'noise_mask'}
return (res, )
class ImagePasteMasked:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"destination": ("IMAGE",),
"source": ("IMAGE",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"resize_source": ("BOOLEAN", {"default": False}),
},
"optional": {
"mask": ("MASK",),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "composite"
CATEGORY = "image"
def composite(self, destination, source, x, y, resize_source, mask = None):
destination = destination.clone().movedim(-1, 1)
output = comfy_extras.nodes_mask.composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
return (output,)
from impact.utils import any_typ
class ImpactLogger:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"data": (any_typ,),
"text": ("STRING", {"multiline": True}),
},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"},
}
CATEGORY = "ImpactPack/Debug"
OUTPUT_NODE = True
RETURN_TYPES = ()
FUNCTION = "doit"
def doit(self, data, text, prompt, extra_pnginfo, unique_id):
shape = ""
if hasattr(data, "shape"):
shape = f"{data.shape} / "
logging.info(f"[IMPACT LOGGER]: {shape}{data}")
logging.info(f" PROMPT: {prompt}")
# for x in prompt:
# if 'inputs' in x and 'populated_text' in x['inputs']:
# 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 {}
class ImpactDummyInput:
@classmethod
def INPUT_TYPES(s):
return {"required": {}}
CATEGORY = "ImpactPack/Debug"
RETURN_TYPES = (any_typ,)
FUNCTION = "doit"
def doit(self):
return ("DUMMY",)
class MasksToMaskList:
@classmethod
def INPUT_TYPES(s):
return {"optional": {
"masks": ("MASK", ),
}
}
RETURN_TYPES = ("MASK", )
OUTPUT_IS_LIST = (True, )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Operation"
def doit(self, masks):
if masks is None:
empty_mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
return ([empty_mask], )
res = []
for mask in masks:
res.append(mask)
res = [make_3d_mask(x) for x in res]
return (res, )
class MaskListToMaskBatch:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"mask": ("MASK", ),
}
}
INPUT_IS_LIST = True
RETURN_TYPES = ("MASK", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Operation"
def doit(self, mask):
if len(mask) == 0:
empty_mask = torch.zeros((1, 64, 64), dtype=torch.float32, device="cpu").unsqueeze(0)
return (empty_mask,)
masks_3d = [make_3d_mask(m) for m in mask]
target_shape = masks_3d[0].shape[1:]
upscaled_masks = []
for m in masks_3d:
if m.shape[1:] != target_shape:
m = m.unsqueeze(1).repeat(1, 3, 1, 1)
m = comfy.utils.common_upscale(m, target_shape[1], target_shape[0], "lanczos", "center")
m = m[:, 0, :, :]
upscaled_masks.append(m)
# Concatenate all at once
result = torch.cat(upscaled_masks, dim=0)
return (result,)
class ImageListToImageBatch:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"images": ("IMAGE", ),
}
}
INPUT_IS_LIST = True
RETURN_TYPES = ("IMAGE", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Operation"
def doit(self, images):
if len(images) == 0:
return ()
if len(images) == 1:
img = images[0]
if img.ndim == 3: # add batch dim if missing
img = img.unsqueeze(0)
return (img,)
# Start with the first image
image1 = images[0]
if image1.ndim == 3:
image1 = image1.unsqueeze(0)
for image2 in images[1:]:
# Ensure batch dim
if image2.ndim == 3:
image2 = image2.unsqueeze(0)
# Ensure same device
if image2.device != image1.device:
image2 = image2.to(image1.device)
# Ensure HxW match exactly
H, W = image1.shape[1], image1.shape[2]
if image2.shape[1] != H or image2.shape[2] != W:
image2 = comfy.utils.common_upscale(
image2.movedim(-1, 1), # move channels first
W, # width
H, # height
"lanczos",
"center"
).movedim(1, -1) # move channels back last
# Ensure channels match
if image2.shape[3] != image1.shape[3]:
# simple fix: truncate or pad channels
min_C = min(image1.shape[3], image2.shape[3])
image1 = image1[:, :, :, :min_C]
image2 = image2[:, :, :, :min_C]
# Concatenate along batch dimension
image1 = torch.cat((image1, image2), dim=0)
return (image1,)
class ImageBatchToImageList:
@classmethod
def INPUT_TYPES(s):
return {"required": {"image": ("IMAGE",), }}
RETURN_TYPES = ("IMAGE",)
OUTPUT_IS_LIST = (True,)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, image):
images = [image[i:i + 1, ...] for i in range(image.shape[0])]
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 NthItemOfAnyList:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"any_list": (any_typ,),
"index": ("INT", {"default": 0, "min": -sys.maxsize, "max": sys.maxsize, "step": 1, "tooltip": "The index of the item you want to select from the list. Use negative values to select from the end (e.g., -1 for last item, -2 for second to last)."}),
}
}
RETURN_TYPES = (any_typ,)
INPUT_IS_LIST = True
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
DESCRIPTION = "Selects the Nth item from a list. If the index is out of range, it returns the last item in the list."
def doit(self, any_list, index):
i = index[0]
list_len = len(any_list)
if i >= list_len or i < -list_len:
return (any_list[-1],)
else:
return (any_list[i],)
class MakeImageList:
@classmethod
def INPUT_TYPES(s):
return {"optional": {"image1": ("IMAGE",), }}
RETURN_TYPES = ("IMAGE",)
OUTPUT_IS_LIST = (True,)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, **kwargs):
images = []
for k, v in kwargs.items():
images.append(v)
return (images, )
class MakeImageBatch:
@classmethod
def INPUT_TYPES(s):
return {"optional": {"image1": ("IMAGE",), }}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, **kwargs):
images = [value for value in kwargs.values()]
if len(images) == 1:
return (images[0],)
else:
image1 = images[0]
for image2 in images[1:]:
if image1.shape[1:] != image2.shape[1:]:
image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "lanczos", "center").movedim(1, -1)
image1 = torch.cat((image1, image2), dim=0)
return (image1,)
class MakeMaskBatch:
@classmethod
def INPUT_TYPES(s):
return {"optional": {"mask1": ("MASK",), }}
RETURN_TYPES = ("MASK",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, **kwargs):
masks = [make_3d_mask(value) for value in kwargs.values()]
if len(masks) == 1:
return (masks[0],)
else:
mask1 = masks[0]
for mask2 in masks[1:]:
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):
return {"required": {
"samples": ("LATENT", ),
"tile_mode": (["None", "Both", "Decode(input) only", "Encode(output) only"],),
"input_vae": ("VAE", ),
"output_vae": ("VAE", ),
"tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}),
},
"optional": {
"overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32, "tooltip": "This setting applies when 'tile_mode' is enabled."}),
}
}
CATEGORY = "ImpactPack/Util"
RETURN_TYPES = ("LATENT", )
FUNCTION = "doit"
def doit(self, samples, tile_mode, input_vae, output_vae, tile_size=512, overlap=64):
if tile_mode in ["Both", "Decode(input) only"]:
decoder = nodes.VAEDecodeTiled()
if 'overlap' in inspect.signature(decoder.decode).parameters:
pixels = decoder.decode(input_vae, samples, tile_size, overlap=overlap)[0]
else:
pixels = decoder.decode(input_vae, samples, tile_size, overlap=overlap)[0]
else:
pixels = nodes.VAEDecode().decode(input_vae, samples)[0]
if tile_mode in ["Both", "Encode(output) only"]:
encoder = nodes.VAEEncodeTiled()
if 'overlap' in inspect.signature(encoder.encode).parameters:
return encoder.encode(output_vae, pixels, tile_size, overlap=overlap)
else:
return encoder.encode(output_vae, pixels, tile_size)
else:
return nodes.VAEEncode().encode(output_vae, pixels)
class ReencodeLatentPipe:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"samples": ("LATENT", ),
"tile_mode": (["None", "Both", "Decode(input) only", "Encode(output) only"],),
"input_basic_pipe": ("BASIC_PIPE", ),
"output_basic_pipe": ("BASIC_PIPE", ),
},
}
CATEGORY = "ImpactPack/Util"
RETURN_TYPES = ("LATENT", )
FUNCTION = "doit"
def doit(self, samples, tile_mode, input_basic_pipe, output_basic_pipe):
_, _, input_vae, _, _ = input_basic_pipe
_, _, output_vae, _, _ = output_basic_pipe
return ReencodeLatent().doit(samples, tile_mode, input_vae, output_vae)
class StringSelector:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"strings": ("STRING", {"multiline": True}),
"multiline": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"select": ("INT", {"min": 0, "max": sys.maxsize, "step": 1, "default": 0}),
}}
RETURN_TYPES = ("STRING",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, strings, multiline, select):
lines = strings.split('\n')
if multiline:
result = []
current_string = ""
for line in lines:
if line.startswith("#"):
if current_string:
result.append(current_string.strip())
current_string = ""
current_string += line + "\n"
if current_string:
result.append(current_string.strip())
if len(result) == 0:
selected = strings
else:
selected = result[select % len(result)]
if selected.startswith('#'):
selected = selected[1:]
else:
if len(lines) == 0:
selected = strings
else:
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)

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custom_nodes/ComfyUI-Impact-Pack/modules/impact/utils.py Normal file
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import torch
import torchvision
import cv2
import numpy as np
import folder_paths
import nodes
from . import config
from PIL import Image
import comfy
import time
import logging
class TensorBatchBuilder:
def __init__(self):
self.tensor = None
def concat(self, new_tensor):
if self.tensor is None:
self.tensor = new_tensor
else:
self.tensor = torch.concat((self.tensor, new_tensor), dim=0)
def tensor_convert_rgba(image, prefer_copy=True):
"""Assumes NHWC format tensor with 1, 3 or 4 channels."""
_tensor_check_image(image)
n_channel = image.shape[-1]
if n_channel == 4:
return image
if n_channel == 3:
alpha = torch.ones((*image.shape[:-1], 1))
return torch.cat((image, alpha), axis=-1)
if n_channel == 1:
if prefer_copy:
image = image.repeat(1, -1, -1, 4)
else:
image = image.expand(1, -1, -1, 3)
return image
# NOTE: Similar error message as in PIL, for easier googling :P
raise ValueError(f"illegal conversion (channels: {n_channel} -> 4)")
def tensor_convert_rgb(image, prefer_copy=True):
"""Assumes NHWC format tensor with 1, 3 or 4 channels."""
_tensor_check_image(image)
n_channel = image.shape[-1]
if n_channel == 3:
return image
if n_channel == 4:
image = image[..., :3]
if prefer_copy:
image = image.copy()
return image
if n_channel == 1:
if prefer_copy:
image = image.repeat(1, -1, -1, 4)
else:
image = image.expand(1, -1, -1, 3)
return image
# NOTE: Same error message as in PIL, for easier googling :P
raise ValueError(f"illegal conversion (channels: {n_channel} -> 3)")
def resize_with_padding(image, target_w: int, target_h: int):
_tensor_check_image(image)
b, h, w, c = image.shape
image = image.permute(0, 3, 1, 2) # B, C, H, W
scale = min(target_w / w, target_h / h)
new_w, new_h = int(w * scale), int(h * scale)
image = F.interpolate(image, size=(new_h, new_w), mode="bilinear", align_corners=False)
pad_left = (target_w - new_w) // 2
pad_right = target_w - new_w - pad_left
pad_top = (target_h - new_h) // 2
pad_bottom = target_h - new_h - pad_top
image = F.pad(image, (pad_left, pad_right, pad_top, pad_bottom), mode='constant', value=0)
image = image.permute(0, 2, 3, 1) # B, H, W, C
return image, (pad_top, pad_bottom, pad_left, pad_right)
def remove_padding(image, padding):
pad_top, pad_bottom, pad_left, pad_right = padding
return image[:, pad_top:image.shape[1] - pad_bottom, pad_left:image.shape[2] - pad_right, :]
def adjust_bbox_after_resize(bbox, original_size, target_size, padding):
"""
bbox: (x1, y1, x2, y2) in original image
original_size: (original_h, original_w)
target_size: (target_h, target_w)
padding: (pad_top, pad_bottom, pad_left, pad_right)
"""
orig_h, orig_w = original_size
target_h, target_w = target_size
pad_top, pad_bottom, pad_left, pad_right = padding
scale = min(target_w / orig_w, target_h / orig_h)
# Apply scale
x1 = int(bbox[0] * scale + pad_left)
y1 = int(bbox[1] * scale + pad_top)
x2 = int(bbox[2] * scale + pad_left)
y2 = int(bbox[3] * scale + pad_top)
return x1, y1, x2, y2
def general_tensor_resize(image, w: int, h: int):
_tensor_check_image(image)
image = image.permute(0, 3, 1, 2)
image = torch.nn.functional.interpolate(image, size=(h, w), mode="bilinear")
image = image.permute(0, 2, 3, 1)
return image
# TODO: Sadly, we need LANCZOS
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
def tensor_resize(image, w: int, h: int):
_tensor_check_image(image)
if image.shape[3] >= 3:
scaled_images = TensorBatchBuilder()
for single_image in image:
single_image = single_image.unsqueeze(0)
single_pil = tensor2pil(single_image)
scaled_pil = single_pil.resize((w, h), resample=LANCZOS)
single_image = pil2tensor(scaled_pil)
scaled_images.concat(single_image)
return scaled_images.tensor
else:
return general_tensor_resize(image, w, h)
def tensor_get_size(image):
"""Mimicking `PIL.Image.size`"""
_tensor_check_image(image)
_, h, w, _ = image.shape
return (w, h)
def tensor2pil(image):
_tensor_check_image(image)
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(0), 0, 255).astype(np.uint8))
def pil2tensor(image):
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
def numpy2pil(image):
return Image.fromarray(np.clip(255. * image.squeeze(0), 0, 255).astype(np.uint8))
def to_pil(image):
if isinstance(image, Image.Image):
return image
if isinstance(image, torch.Tensor):
return tensor2pil(image)
if isinstance(image, np.ndarray):
return numpy2pil(image)
raise ValueError(f"Cannot convert {type(image)} to PIL.Image")
def to_tensor(image):
if isinstance(image, Image.Image):
return torch.from_numpy(np.array(image)) / 255.0
if isinstance(image, torch.Tensor):
return image
if isinstance(image, np.ndarray):
return torch.from_numpy(image)
raise ValueError(f"Cannot convert {type(image)} to torch.Tensor")
def to_numpy(image):
if isinstance(image, Image.Image):
return np.array(image)
if isinstance(image, torch.Tensor):
return image.numpy()
if isinstance(image, np.ndarray):
return image
raise ValueError(f"Cannot convert {type(image)} to numpy.ndarray")
def tensor_putalpha(image, mask):
_tensor_check_image(image)
_tensor_check_mask(mask)
image[..., -1] = mask[..., 0]
def _tensor_check_image(image):
if image.ndim != 4:
raise ValueError(f"Expected NHWC tensor, but found {image.ndim} dimensions")
if image.shape[-1] not in (1, 3, 4):
raise ValueError(f"Expected 1, 3 or 4 channels for image, but found {image.shape[-1]} channels")
return
def _tensor_check_mask(mask):
if mask.ndim != 4:
raise ValueError(f"Expected NHWC tensor, but found {mask.ndim} dimensions")
if mask.shape[-1] != 1:
raise ValueError(f"Expected 1 channel for mask, but found {mask.shape[-1]} channels")
return
def tensor_crop(image, crop_region):
_tensor_check_image(image)
return crop_ndarray4(image, crop_region)
def tensor2numpy(image):
_tensor_check_image(image)
return image.numpy()
def tensor_paste(image1, image2, left_top, mask):
"""
Pastes image2 onto image1 at position left_top using mask.
Supports both RGB and RGBA images.
"""
_tensor_check_image(image1)
_tensor_check_image(image2)
_tensor_check_mask(mask)
if image2.shape[1:3] != mask.shape[1:3]:
mask = resize_mask(mask.squeeze(dim=3), image2.shape[1:3]).unsqueeze(dim=3)
x, y = left_top
_, h1, w1, c1 = image1.shape
_, h2, w2, c2 = image2.shape
# Calculate image patch size
w = min(w1, x + w2) - x
h = min(h1, y + h2) - y
# If the patch is out of bound, nothing to do!
if w <= 0 or h <= 0:
return
mask = mask[:, :h, :w, :]
# Get the region to be modified
region1 = image1[:, y:y+h, x:x+w, :]
region2 = image2[:, :h, :w, :]
# Handle RGB and RGBA cases
if c1 == 3 and c2 == 3:
# Both RGB - simple case
image1[:, y:y+h, x:x+w, :] = (1 - mask) * region1 + mask * region2
elif c1 == 4 and c2 == 4:
# Both RGBA - need to handle alpha channel separately
# RGB channels
image1[:, y:y+h, x:x+w, :3] = (
(1 - mask) * region1[:, :, :, :3] +
mask * region2[:, :, :, :3]
)
# Alpha channel - use "over" composition
a1 = region1[:, :, :, 3:4]
a2 = region2[:, :, :, 3:4] * mask
new_alpha = a1 + a2 * (1 - a1)
image1[:, y:y+h, x:x+w, 3:4] = new_alpha
elif c1 == 4 and c2 == 3:
# Target is RGBA, source is RGB - assume source is fully opaque
image1[:, y:y+h, x:x+w, :3] = (
(1 - mask) * region1[:, :, :, :3] +
mask * region2
)
# Alpha channel - reduce alpha where mask is applied
image1[:, y:y+h, x:x+w, 3:4] = region1[:, :, :, 3:4] * (1 - mask) + mask
elif c1 == 3 and c2 == 4:
# Target is RGB, source is RGBA - apply source alpha to mask
effective_mask = mask * region2[:, :, :, 3:4]
image1[:, y:y+h, x:x+w, :] = (
(1 - effective_mask) * region1 +
effective_mask * region2[:, :, :, :3]
)
return
def center_of_bbox(bbox):
w, h = bbox[2] - bbox[0], bbox[3] - bbox[1]
return bbox[0] + w/2, bbox[1] + h/2
def combine_masks(masks):
if len(masks) == 0:
return None
else:
initial_cv2_mask = np.array(masks[0][1])
combined_cv2_mask = initial_cv2_mask
for i in range(1, len(masks)):
cv2_mask = np.array(masks[i][1])
if combined_cv2_mask.shape == cv2_mask.shape:
combined_cv2_mask = cv2.bitwise_or(combined_cv2_mask, cv2_mask)
else:
# do nothing - incompatible mask
pass
mask = torch.from_numpy(combined_cv2_mask)
return mask
def combine_masks2(masks):
if len(masks) == 0:
return None
else:
initial_cv2_mask = np.array(masks[0]).astype(np.uint8)
combined_cv2_mask = initial_cv2_mask
for i in range(1, len(masks)):
cv2_mask = np.array(masks[i]).astype(np.uint8)
if combined_cv2_mask.shape == cv2_mask.shape:
combined_cv2_mask = cv2.bitwise_or(combined_cv2_mask, cv2_mask)
else:
# do nothing - incompatible mask
pass
mask = torch.from_numpy(combined_cv2_mask)
return mask
def bitwise_and_masks(mask1, mask2):
mask1 = mask1.cpu()
mask2 = mask2.cpu()
cv2_mask1 = np.array(mask1)
cv2_mask2 = np.array(mask2)
if cv2_mask1.shape == cv2_mask2.shape:
cv2_mask = cv2.bitwise_and(cv2_mask1, cv2_mask2)
return torch.from_numpy(cv2_mask)
else:
# do nothing - incompatible mask shape: mostly empty mask
return mask1
def to_binary_mask(mask, threshold=0):
mask = make_3d_mask(mask)
mask = mask.clone().cpu()
mask[mask > threshold] = 1.
mask[mask <= threshold] = 0.
return mask
def use_gpu_opencv():
return not config.get_config()['disable_gpu_opencv']
def dilate_mask(mask, dilation_factor, iter=1):
if dilation_factor == 0:
return make_2d_mask(mask)
mask = make_2d_mask(mask)
kernel = np.ones((abs(dilation_factor), abs(dilation_factor)), np.uint8)
if use_gpu_opencv():
mask = cv2.UMat(mask)
kernel = cv2.UMat(kernel)
if dilation_factor > 0:
result = cv2.dilate(mask, kernel, iter)
else:
result = cv2.erode(mask, kernel, iter)
if use_gpu_opencv():
return result.get()
else:
return result
def dilate_masks(segmasks, dilation_factor, iter=1):
if dilation_factor == 0:
return segmasks
dilated_masks = []
kernel = np.ones((abs(dilation_factor), abs(dilation_factor)), np.uint8)
if use_gpu_opencv():
kernel = cv2.UMat(kernel)
for i in range(len(segmasks)):
cv2_mask = segmasks[i][1]
if use_gpu_opencv():
cv2_mask = cv2.UMat(cv2_mask)
if dilation_factor > 0:
dilated_mask = cv2.dilate(cv2_mask, kernel, iter)
else:
dilated_mask = cv2.erode(cv2_mask, kernel, iter)
if use_gpu_opencv():
dilated_mask = dilated_mask.get()
item = (segmasks[i][0], dilated_mask, segmasks[i][2])
dilated_masks.append(item)
return dilated_masks
import torch.nn.functional as F
def feather_mask(mask, thickness):
mask = mask.permute(0, 3, 1, 2)
# Gaussian kernel for blurring
kernel_size = 2 * int(thickness) + 1
sigma = thickness / 3 # Adjust the sigma value as needed
blur_kernel = _gaussian_kernel(kernel_size, sigma).to(mask.device, mask.dtype)
# Apply blur to the mask
blurred_mask = F.conv2d(mask, blur_kernel.unsqueeze(0).unsqueeze(0), padding=thickness)
blurred_mask = blurred_mask.permute(0, 2, 3, 1)
return blurred_mask
def _gaussian_kernel(kernel_size, sigma):
# Generate a 1D Gaussian kernel
kernel = torch.exp(-(torch.arange(kernel_size) - kernel_size // 2)**2 / (2 * sigma**2))
return kernel / kernel.sum()
def tensor_gaussian_blur_mask(mask, kernel_size, sigma=10.0):
"""Return NHWC torch.Tenser from ndim == 2 or 4 `np.ndarray` or `torch.Tensor`"""
if isinstance(mask, np.ndarray):
mask = torch.from_numpy(mask)
if mask.ndim == 2:
mask = mask[None, ..., None]
elif mask.ndim == 3:
mask = mask[..., None]
_tensor_check_mask(mask)
if kernel_size <= 0:
return mask
kernel_size = kernel_size*2+1
shortest = min(mask.shape[1], mask.shape[2])
if shortest <= kernel_size:
kernel_size = int(shortest/2)
if kernel_size % 2 == 0:
kernel_size += 1
if kernel_size < 3:
return mask # skip feathering
prev_device = mask.device
device = comfy.model_management.get_torch_device()
mask.to(device)
# apply gaussian blur
mask = mask[:, None, ..., 0]
blurred_mask = torchvision.transforms.GaussianBlur(kernel_size=kernel_size, sigma=sigma)(mask)
blurred_mask = blurred_mask[:, 0, ..., None]
blurred_mask.to(prev_device)
return blurred_mask
def subtract_masks(mask1, mask2):
mask1 = mask1.cpu()
mask2 = mask2.cpu()
cv2_mask1 = np.array(mask1) * 255
cv2_mask2 = np.array(mask2) * 255
if cv2_mask1.shape == cv2_mask2.shape:
cv2_mask = cv2.subtract(cv2_mask1, cv2_mask2)
return torch.clamp(torch.from_numpy(cv2_mask) / 255.0, min=0, max=1)
else:
# do nothing - incompatible mask shape: mostly empty mask
return mask1
def add_masks(mask1, mask2):
mask1 = mask1.cpu()
mask2 = mask2.cpu()
cv2_mask1 = np.array(mask1) * 255
cv2_mask2 = np.array(mask2) * 255
if cv2_mask1.shape == cv2_mask2.shape:
cv2_mask = cv2.add(cv2_mask1, cv2_mask2)
return torch.clamp(torch.from_numpy(cv2_mask) / 255.0, min=0, max=1)
else:
# do nothing - incompatible mask shape: mostly empty mask
return mask1
def normalize_region(limit, startp, size):
if startp < 0:
new_endp = min(limit, size)
new_startp = 0
elif startp + size > limit:
new_startp = max(0, limit - size)
new_endp = limit
else:
new_startp = startp
new_endp = min(limit, startp+size)
return int(new_startp), int(new_endp)
def make_crop_region(w, h, bbox, crop_factor, crop_min_size=None):
x1 = bbox[0]
y1 = bbox[1]
x2 = bbox[2]
y2 = bbox[3]
bbox_w = x2 - x1
bbox_h = y2 - y1
crop_w = bbox_w * crop_factor
crop_h = bbox_h * crop_factor
if crop_min_size is not None:
crop_w = max(crop_min_size, crop_w)
crop_h = max(crop_min_size, crop_h)
kernel_x = x1 + bbox_w / 2
kernel_y = y1 + bbox_h / 2
new_x1 = int(kernel_x - crop_w / 2)
new_y1 = int(kernel_y - crop_h / 2)
# make sure position in (w,h)
new_x1, new_x2 = normalize_region(w, new_x1, crop_w)
new_y1, new_y2 = normalize_region(h, new_y1, crop_h)
return [new_x1, new_y1, new_x2, new_y2]
def crop_ndarray4(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
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]
x2 = crop_region[2]
y2 = crop_region[3]
cropped = npimg[y1:y2, x1:x2]
return cropped
def crop_image(image, crop_region):
return crop_tensor4(image, crop_region)
def to_latent_image(pixels, vae, vae_tiled_encode=False):
x = pixels.shape[1]
y = pixels.shape[2]
if pixels.shape[1] != x or pixels.shape[2] != y:
pixels = pixels[:, :x, :y, :]
start = time.time()
if vae_tiled_encode:
encoded = nodes.VAEEncodeTiled().encode(vae, pixels, 512, overlap=64)[0] # using default settings
logging.info(f"[Impact Pack] vae encoded (tiled) in {time.time() - start:.1f}s")
else:
encoded = nodes.VAEEncode().encode(vae, pixels)[0]
logging.info(f"[Impact Pack] vae encoded in {time.time() - start:.1f}s")
return encoded
def empty_pil_tensor(w=64, h=64):
return torch.zeros((1, h, w, 3), dtype=torch.float32)
def make_2d_mask(mask):
if len(mask.shape) == 4:
return mask.squeeze(0).squeeze(0)
elif len(mask.shape) == 3:
return mask.squeeze(0)
return mask
def make_3d_mask(mask):
if len(mask.shape) == 4:
return mask.squeeze(0)
elif len(mask.shape) == 2:
return mask.unsqueeze(0)
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
return a_device.type == b_device.type and a_device.index == b_device.index
def collect_non_reroute_nodes(node_map, links, res, node_id):
if node_map[node_id]['type'] != 'Reroute' and node_map[node_id]['type'] != 'Reroute (rgthree)':
res.append(node_id)
else:
for link in node_map[node_id]['outputs'][0]['links']:
next_node_id = str(links[link][2])
collect_non_reroute_nodes(node_map, links, res, next_node_id)
from torchvision.transforms.functional import to_pil_image
def resize_mask(mask, size):
mask = make_4d_mask(mask)
resized_mask = torch.nn.functional.interpolate(mask, size=size, mode='bilinear', align_corners=False)
return resized_mask.squeeze(0)
def apply_mask_alpha_to_pil(decoded_pil, mask):
decoded_rgba = decoded_pil.convert('RGBA')
mask_pil = to_pil_image(mask)
decoded_rgba.putalpha(mask_pil)
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
cm_global.try_call(api='cm.try-install-custom-node',
sender="Impact Pack", custom_node_url=custom_node_url, msg=msg)
except Exception:
logging.info(msg)
logging.info("[Impact Pack] ComfyUI-Manager is outdated. The custom node installation feature is not available.")
# author: Trung0246 --->
class TautologyStr(str):
def __ne__(self, other):
return False
class ByPassTypeTuple(tuple):
def __getitem__(self, index):
if index > 0:
index = 0
item = super().__getitem__(index)
if isinstance(item, str):
return TautologyStr(item)
return item
class NonListIterable:
def __init__(self, data):
self.data = data
def __getitem__(self, index):
return self.data[index]
def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions):
# Iterate over the list of full folder paths
for full_folder_path in full_folder_paths:
# Use the provided function to add each model folder path
folder_paths.add_model_folder_path(folder_name, full_folder_path)
# Now handle the extensions. If the folder name already exists, update the extensions
if folder_name in folder_paths.folder_names_and_paths:
# Unpack the current paths and extensions
current_paths, current_extensions = folder_paths.folder_names_and_paths[folder_name]
# Update the extensions set with the new extensions
updated_extensions = current_extensions | extensions
# Reassign the updated tuple back to the dictionary
folder_paths.folder_names_and_paths[folder_name] = (current_paths, updated_extensions)
else:
# If the folder name was not present, add_model_folder_path would have added it with the last path
# Now we just need to update the set of extensions as it would be an empty set
# Also ensure that all paths are included (since add_model_folder_path adds only one path at a time)
folder_paths.folder_names_and_paths[folder_name] = (full_folder_paths, extensions)
# <---
# wildcard trick is taken from pythongossss's
class AnyType(str):
def __ne__(self, __value: object) -> bool:
return False
any_typ = AnyType("*")

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custom_nodes/ComfyUI-Impact-Pack/modules/thirdparty/noise_nodes.py vendored Normal file
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# Due to the current lack of maintenance for the `ComfyUI_Noise` extension,
# I have copied the code from the applied PR.
# https://github.com/BlenderNeko/ComfyUI_Noise/pull/13/files
import comfy
import torch
class Unsampler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"end_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
"cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
"normalize": (["disable", "enable"],),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"latent_image": ("LATENT",),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "unsampler"
CATEGORY = "sampling"
def unsampler(self, model, cfg, sampler_name, steps, end_at_step, scheduler, normalize, positive, negative,
latent_image):
normalize = normalize == "enable"
device = comfy.model_management.get_torch_device()
latent = latent_image
latent_image = latent["samples"]
end_at_step = min(end_at_step, steps - 1)
end_at_step = steps - end_at_step
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.sampler_helpers.prepare_mask(latent["noise_mask"], noise.shape, device)
noise = noise.to(device)
latent_image = latent_image.to(device)
conds0 = \
{"positive": comfy.sampler_helpers.convert_cond(positive),
"negative": comfy.sampler_helpers.convert_cond(negative)}
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(model, steps=steps, device=device, sampler=sampler_name,
scheduler=scheduler, denoise=1.0, model_options=model.model_options)
sigmas = sampler.sigmas.flip(0) + 0.0001
pbar = comfy.utils.ProgressBar(steps)
def callback(step, x0, x, total_steps):
pbar.update_absolute(step + 1, total_steps)
samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image,
force_full_denoise=False, denoise_mask=noise_mask, sigmas=sigmas, start_step=0,
last_step=end_at_step, callback=callback)
if normalize:
# technically doesn't normalize because unsampling is not guaranteed to end at a std given by the schedule
samples -= samples.mean()
samples /= samples.std()
samples = samples.cpu()
comfy.sampler_helpers.cleanup_additional_models(models)
out = latent.copy()
out["samples"] = samples
return (out,)

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custom_nodes/ComfyUI-Impact-Pack/node_list.json Normal file
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{
"Segs Mask": "This node is renamed to 'ImpactSegsAndMask'",
"Segs Mask ForEach": "This node is renamed to 'ImpactSegsAndMaskForEach'"
}

172
custom_nodes/ComfyUI-Impact-Pack/notebook/comfyui_colab_impact_pack.ipynb Normal file
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "aaaaaaaaaa"
},
"source": [
"Git clone the repo and install the requirements. (ignore the pip errors about protobuf)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bbbbbbbbbb"
},
"outputs": [],
"source": [
"#@title Environment Setup\n",
"\n",
"from pathlib import Path\n",
"\n",
"OPTIONS = {}\n",
"\n",
"WORKSPACE = 'ComfyUI'\n",
"USE_GOOGLE_DRIVE = True #@param {type:\"boolean\"}\n",
"UPDATE_COMFY_UI = True #@param {type:\"boolean\"}\n",
"\n",
"OPTIONS['USE_GOOGLE_DRIVE'] = USE_GOOGLE_DRIVE\n",
"OPTIONS['UPDATE_COMFY_UI'] = UPDATE_COMFY_UI\n",
"\n",
"if OPTIONS['USE_GOOGLE_DRIVE']:\n",
" !echo \"Mounting Google Drive...\"\n",
" %cd /\n",
" \n",
" from google.colab import drive\n",
" drive.mount('/content/drive')\n",
"\n",
" WORKSPACE = \"/content/drive/MyDrive/ComfyUI\"\n",
" \n",
" %cd /content/drive/MyDrive\n",
"\n",
"![ ! -d $WORKSPACE ] && echo \"-= Initial setup ComfyUI (Original)=-\" && git clone https://github.com/comfyanonymous/ComfyUI\n",
"%cd $WORKSPACE\n",
"\n",
"if OPTIONS['UPDATE_COMFY_UI']:\n",
" !echo \"-= Updating ComfyUI =-\"\n",
" !git pull\n",
" !rm \"/content/drive/MyDrive/ComfyUI/custom_nodes/comfyui-impact-pack.py\"\n",
"\n",
"%cd custom_nodes\n",
"!git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack\n",
"%cd $WORKSPACE\n",
"\n",
"!echo -= Install dependencies =-\n",
"!pip -q install xformers -r requirements.txt\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "kkkkkkkkkkkkkk"
},
"source": [
"### Run ComfyUI with localtunnel (Recommended Way)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jjjjjjjjjjjjj",
"outputId": "83be9411-d939-4813-e6c1-80e75bf8e80d"
},
"outputs": [],
"source": [
"!npm install -g localtunnel\n",
"\n",
"import subprocess\n",
"import threading\n",
"import time\n",
"import socket\n",
"def iframe_thread(port):\n",
" while True:\n",
" time.sleep(0.5)\n",
" sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
" result = sock.connect_ex(('127.0.0.1', port))\n",
" if result == 0:\n",
" break\n",
" sock.close()\n",
" print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\")\n",
" p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n",
" for line in p.stdout:\n",
" print(line.decode(), end='')\n",
"\n",
"\n",
"threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n",
"\n",
"!python main.py --dont-print-server"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "gggggggggg"
},
"source": [
"### Run ComfyUI with colab iframe (use only in case the previous way with localtunnel doesn't work)\n",
"\n",
"You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n",
"\n",
"If you want to open it in another window use the link.\n",
"\n",
"Note that some UI features like live image previews won't work because the colab iframe blocks websockets."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hhhhhhhhhh"
},
"outputs": [],
"source": [
"import threading\n",
"import time\n",
"import socket\n",
"def iframe_thread(port):\n",
" while True:\n",
" time.sleep(0.5)\n",
" sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
" result = sock.connect_ex(('127.0.0.1', port))\n",
" if result == 0:\n",
" break\n",
" sock.close()\n",
" from google.colab import output\n",
" output.serve_kernel_port_as_iframe(port, height=1024)\n",
" print(\"to open it in a window you can open this link here:\")\n",
" output.serve_kernel_port_as_window(port)\n",
"\n",
"threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n",
"\n",
"!python main.py --dont-print-server"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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custom_nodes/ComfyUI-Impact-Pack/pyproject.toml Normal file
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[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 = "8.28.2"
license = { file = "LICENSE.txt" }
dependencies = [
"segment-anything",
"scikit-image",
"piexif",
"transformers",
"opencv-python-headless",
"scipy",
"numpy",
"dill",
"matplotlib",
"sam2 @ git+https://github.com/facebookresearch/sam2"
]
[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 = ""

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custom_nodes/ComfyUI-Impact-Pack/requirements.txt Normal file
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segment-anything
scikit-image
piexif
transformers
opencv-python-headless
scipy
numpy
dill
matplotlib
git+https://github.com/facebookresearch/sam2

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custom_nodes/ComfyUI-Impact-Pack/ruff.toml Normal file
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[lint]
ignore = ["E402","E701"]
exclude = ["install.py", "*.ipynb"]

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custom_nodes/ComfyUI-Impact-Pack/tests/README.md Normal file
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# Wildcard System Test Suite
Comprehensive test suite for ComfyUI Impact Pack wildcard system.
## Test Suites
### test_encoding.sh (15 tests)
**Purpose**: UTF-8 multi-language encoding validation
**Port**: 8198
**Coverage**:
- Korean Hangul characters
- Emoji support
- Chinese characters
- Arabic RTL text
- Mathematical and currency symbols
- Mixed multi-language content
- UTF-8 in dynamic prompts, quantifiers, multi-select
### test_error_handling.sh (10 tests)
**Purpose**: Graceful error handling verification
**Port**: 8197
**Coverage**:
- Non-existent wildcards
- Circular reference detection (max 100 iterations)
- Malformed syntax
- Deep nesting without crashes
- Multiple circular references
### test_edge_cases.sh (20 tests)
**Purpose**: Edge case and boundary condition validation
**Port**: 8196
**Coverage**:
- Empty lines and whitespace filtering
- Very long lines (>1000 characters)
- Special characters preservation
- Case-insensitive matching
- Comment line filtering
- Pattern matching (__*/name__)
- Quantifiers (N#__wildcard__)
- Complex syntax combinations
### test_deep_nesting.sh (17 tests)
**Purpose**: Transitive wildcard expansion and depth-agnostic pattern matching
**Port**: 8194
**Coverage**:
- 7-level transitive expansion (directory depth + file references)
- All depth levels (1-7) individually
- Mixed depth combinations
- Nesting with quantifiers and multi-select
- Nesting with weighted selection
- Depth-agnostic pattern matching (`__*/name__`)
- Complex multi-wildcard prompts
### test_ondemand_loading.sh (8 tests)
**Purpose**: Progressive on-demand wildcard loading
**Port**: 8193
**Coverage**:
- Small cache (1MB) - on-demand enabled
- Moderate cache (10MB) - progressive loading
- Large cache (100MB) - eager loading
- Aggressive lazy loading (0.5MB)
- Balanced mode (50MB default)
- On-demand with deep nesting
- On-demand with multiple wildcards
- Cache boundary testing
### test_config_quotes.sh (5 tests)
**Purpose**: Configuration path handling validation
**Port**: 8192
**Coverage**:
- Unquoted paths
- Double-quoted paths
- Single-quoted paths
- Paths with spaces
- Mixed quote scenarios
### test_dynamic_prompts_full.sh (11 tests)
**Purpose**: Comprehensive dynamic prompt feature validation with statistical analysis
**Port**: 8188
**Coverage**:
- **Multiselect** (4 tests): 2-item, 3-item, single-item, max-item with separator validation
- **Weighted Selection** (5 tests): 10:1 ratio, equal weights, extreme bias, multi-level weights, default mixing
- **Basic Selection** (2 tests): Simple random, nested selection
- Statistical distribution verification (100+ iterations per test)
- Duplicate detection and item count validation
- Separator correctness validation
## Quick Start
```bash
# Run individual test
bash test_encoding.sh
# Run all tests
bash test_encoding.sh
bash test_error_handling.sh
bash test_edge_cases.sh
bash test_deep_nesting.sh
bash test_ondemand_loading.sh
bash test_config_quotes.sh
bash test_dynamic_prompts_full.sh
```
## Test Infrastructure
- **Configuration**: Each test creates `impact-pack.ini` with test wildcard path
- **Server Lifecycle**: Automatic server start/stop with dedicated ports
- **Cleanup**: Automatic cleanup on test completion
- **Logging**: Detailed logs in `/tmp/*_test.log`
## Test Samples
Located in `wildcards/samples/`:
- `아름다운색.txt` - Korean UTF-8 test with 12 symbolic colors
- `test_encoding_*.txt` - UTF-8 encoding test files
- `test_edge_*.txt` - Edge case test files
- `test_error_*.txt` - Error handling test files
- `test_nesting_*.txt` - Nesting test files (7 levels)
- `patterns/` - Subdirectory for pattern matching tests
## Status
✅ **86 tests, 100% pass rate** (15+10+20+17+8+5+11)
✅ **Production ready**
✅ **Complete PRD coverage**
✅ **On-demand loading validated**
✅ **Config quotes handling validated**
✅ **Dynamic prompts statistically validated**
✅ **Weighted selection verified (correct {weight::option} syntax)**
✅ **Pattern matching validated (depth-agnostic __*/name__)**
## Documentation
- [Wildcard System PRD](../docs/wildcards/WILDCARD_SYSTEM_PRD.md)
- [System Design](../docs/wildcards/WILDCARD_SYSTEM_DESIGN.md)
- [Testing Guide](../docs/wildcards/WILDCARD_TESTING_GUIDE.md)

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# Run All Tests
Execute the complete wildcard system test suite.
## Quick Run
```bash
cd /mnt/teratera/git/ComfyUI/custom_nodes/comfyui-impact-pack/tests
bash test_encoding.sh && \
bash test_error_handling.sh && \
bash test_edge_cases.sh && \
bash test_deep_nesting.sh && \
bash test_ondemand_loading.sh && \
bash test_config_quotes.sh && \
bash test_dynamic_prompts_full.sh
echo ""
echo "=========================================="
echo "Test Suite Complete"
echo "=========================================="
echo "Total: 86 tests across 7 suites"
echo ""
```
## Individual Tests
```bash
# UTF-8 Encoding (15 tests)
bash test_encoding.sh
# Error Handling (10 tests)
bash test_error_handling.sh
# Edge Cases (20 tests)
bash test_edge_cases.sh
# Deep Nesting (15 tests)
bash test_deep_nesting.sh
# On-Demand Loading (8 tests)
bash test_ondemand_loading.sh
# Config Quotes (5 tests)
bash test_config_quotes.sh
# Dynamic Prompts Full (11 tests)
bash test_dynamic_prompts_full.sh
```
## Test Summary
Each test suite:
- ✅ Starts dedicated ComfyUI server on unique port
- ✅ Configures test wildcard path
- ✅ Runs comprehensive test cases
- ✅ Validates results
- ✅ Cleans up automatically
## Expected Results
All 89 tests should pass (100% pass rate).
## Logs
Test logs are saved in `/tmp/`:
- `/tmp/encoding_test.log`
- `/tmp/error_handling_test.log`
- `/tmp/edge_cases_test.log`
- `/tmp/deep_nesting_test.log`
- `/tmp/ondemand_test.log`
- `/tmp/config_quotes_test.log`
- `/tmp/dynamic_prompt_full_validation.log`

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#!/bin/bash
# restart_test_server.sh
# ComfyUI 서버를 빠르게 재시작하는 유틸리티 스크립트
# Usage: bash restart_test_server.sh [PORT]
PORT=${1:-8188} # 기본 포트 8188
COMFYUI_DIR="/mnt/teratera/git/ComfyUI"
LOG_FILE="/tmp/comfyui_test_${PORT}.log"
echo "=========================================="
echo "ComfyUI Test Server Restart Utility"
echo "=========================================="
echo "Port: $PORT"
echo "Log: $LOG_FILE"
echo ""
# 1. 기존 서버 종료
echo "🛑 Stopping existing server..."
pkill -f "python.*main.py"
sleep 2
# 프로세스 종료 확인
if pgrep -f "python.*main.py" > /dev/null; then
echo "⚠️ Warning: Some processes still running"
ps aux | grep main.py | grep -v grep
echo "Forcing kill..."
pkill -9 -f "python.*main.py"
sleep 1
fi
echo "✅ Server stopped"
# 2. 서버 시작
echo ""
echo "🚀 Starting server on port $PORT..."
cd "$COMFYUI_DIR" || {
echo "❌ Error: Cannot access $COMFYUI_DIR"
exit 1
}
# 백그라운드로 서버 시작
bash run.sh --listen 127.0.0.1 --port "$PORT" > "$LOG_FILE" 2>&1 &
SERVER_PID=$!
echo "Server PID: $SERVER_PID"
echo ""
# 3. 서버 준비 대기
echo "⏳ Waiting for server startup..."
for i in {1..30}; do
sleep 1
if curl -s http://127.0.0.1:$PORT/ > /dev/null 2>&1; then
echo ""
echo "✅ Server ready on port $PORT (${i}s)"
echo "📝 Log: $LOG_FILE"
echo "🔗 URL: http://127.0.0.1:$PORT"
echo ""
echo "Test endpoints:"
echo " curl http://127.0.0.1:$PORT/impact/wildcards/list"
echo " curl http://127.0.0.1:$PORT/impact/wildcards/list/loaded"
exit 0
fi
echo -n "."
done
# 타임아웃
echo ""
echo "❌ Server failed to start within 30 seconds"
echo "📝 Check log: $LOG_FILE"
echo ""
echo "Last 20 lines of log:"
tail -20 "$LOG_FILE"
exit 1

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#!/bin/bash
# Config Path Quotes Test Suite
# Tests handling of quoted paths in impact-pack.ini
set -e
PORT=8192
COMFYUI_DIR="/mnt/teratera/git/ComfyUI"
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMPACT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
CONFIG_FILE="$IMPACT_DIR/impact-pack.ini"
LOG_FILE="/tmp/config_quotes_test.log"
GREEN='\033[0;32m'
RED='\033[0;31m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m'
echo "=========================================="
echo "Config Path Quotes Test Suite"
echo "=========================================="
echo "Port: $PORT"
echo "Testing: Quoted path handling in config"
echo ""
# Cleanup function
cleanup() {
echo ""
echo "Cleaning up..."
pkill -f "python.*main.py.*--port $PORT" 2>/dev/null || true
rm -f "$CONFIG_FILE"
echo "Cleanup complete"
}
trap cleanup EXIT
# Test function
test_config_format() {
local TEST_NUM=$1
local DESCRIPTION=$2
local PATH_VALUE=$3
local PROMPT=$4
local SEED=$5
echo "${BLUE}=== Test $TEST_NUM: $DESCRIPTION ===${NC}"
echo "Path format: ${YELLOW}$PATH_VALUE${NC}"
# Kill existing server
pkill -f "python.*main.py.*--port $PORT" 2>/dev/null || true
sleep 2
# Create config with specific path format
cat > "$CONFIG_FILE" << EOF
[default]
custom_wildcards = $PATH_VALUE
wildcard_cache_limit_mb = 50
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
disable_gpu_opencv = True
EOF
echo "Config created:"
grep "custom_wildcards" "$CONFIG_FILE"
# Start server
cd "$COMFYUI_DIR"
bash run.sh --listen 127.0.0.1 --port $PORT > "$LOG_FILE" 2>&1 &
SERVER_PID=$!
# Wait for server
for i in {1..60}; do
sleep 1
if curl -s http://127.0.0.1:$PORT/ > /dev/null 2>&1; then
echo "✅ Server ready (${i}s)"
break
fi
if [ $i -eq 60 ]; then
echo "${RED}❌ Server failed to start${NC}"
echo "Log tail:"
tail -20 "$LOG_FILE"
exit 1
fi
done
# Test wildcard expansion
RESULT=$(curl -s -X POST http://127.0.0.1:$PORT/impact/wildcards \
-H "Content-Type: application/json" \
-d "{\"text\": \"$PROMPT\", \"seed\": $SEED}" | \
python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))" 2>/dev/null || echo "ERROR")
echo "Result: ${GREEN}$RESULT${NC}"
if [ "$RESULT" != "ERROR" ] && [ -n "$RESULT" ] && ! echo "$RESULT" | grep -q "__"; then
echo "Status: ${GREEN}✅ PASS - Path correctly handled${NC}"
else
echo "Status: ${RED}❌ FAIL - Path not working${NC}"
echo "Checking log for errors..."
grep -i "custom_wildcards\|wildcard" "$LOG_FILE" | tail -5
fi
echo ""
}
echo "=========================================="
echo "Test Suite Execution"
echo "=========================================="
echo ""
# Test 1: No quotes (standard)
test_config_format "01" "No quotes (standard)" \
"$IMPACT_DIR/tests/wildcards/samples" \
"__아름다운색__" \
100
# Test 2: Double quotes
test_config_format "02" "Double quotes" \
"\"$IMPACT_DIR/tests/wildcards/samples\"" \
"__아름다운색__" \
200
# Test 3: Single quotes
test_config_format "03" "Single quotes" \
"'$IMPACT_DIR/tests/wildcards/samples'" \
"__아름다운색__" \
300
# Test 4: Mixed quotes (edge case)
test_config_format "04" "Path with spaces (double quotes)" \
"\"$IMPACT_DIR/tests/wildcards/samples\"" \
"__test_nesting_level1__" \
400
# Test 5: Absolute path no quotes
test_config_format "05" "Absolute path no quotes" \
"$IMPACT_DIR/tests/wildcards/samples" \
"__test_encoding_emoji__" \
500
echo ""
echo "=========================================="
echo "Summary"
echo "=========================================="
echo "${GREEN}✅ Config quotes tests completed${NC}"
echo ""
echo "Test results:"
echo " 1. No quotes (standard) ✓"
echo " 2. Double quotes ✓"
echo " 3. Single quotes ✓"
echo " 4. Path with spaces ✓"
echo " 5. Absolute path ✓"
echo ""
echo "Quote handling verified:"
echo " - Strip double quotes (\") ✓"
echo " - Strip single quotes (') ✓"
echo " - Handle unquoted paths ✓"
echo ""
echo "Log file: $LOG_FILE"

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#!/bin/bash
# Deep Nesting Test Suite
# Tests transitive wildcard expansion up to 7 levels
set -e
PORT=8194
COMFYUI_DIR="/mnt/teratera/git/ComfyUI"
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMPACT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
CONFIG_FILE="$IMPACT_DIR/impact-pack.ini"
LOG_FILE="/tmp/deep_nesting_test.log"
GREEN='\033[0;32m'
RED='\033[0;31m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
CYAN='\033[0;36m'
NC='\033[0m'
echo "=========================================="
echo "Deep Nesting Test Suite (7 Levels)"
echo "=========================================="
echo "Port: $PORT"
echo "Testing: Transitive wildcard expansion"
echo ""
# Cleanup function
cleanup() {
echo ""
echo "Cleaning up..."
pkill -f "python.*main.py.*--port $PORT" 2>/dev/null || true
rm -f "$CONFIG_FILE"
echo "Cleanup complete"
}
trap cleanup EXIT
# Kill any existing server on this port
echo "Killing any existing server on port $PORT..."
pkill -f "python.*main.py.*--port $PORT" 2>/dev/null || true
sleep 2
# Setup configuration
echo "Setting up configuration..."
cat > "$CONFIG_FILE" << EOF
[default]
custom_wildcards = $IMPACT_DIR/tests/wildcards/samples
wildcard_cache_limit_mb = 50
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
disable_gpu_opencv = True
EOF
echo "Configuration created: custom_wildcards = $IMPACT_DIR/tests/wildcards/samples"
echo ""
# Start server
echo "Starting ComfyUI server on port $PORT..."
cd "$COMFYUI_DIR"
bash run.sh --listen 127.0.0.1 --port $PORT > "$LOG_FILE" 2>&1 &
SERVER_PID=$!
echo "Server PID: $SERVER_PID"
# Wait for server startup
echo "Waiting for server startup..."
for i in {1..60}; do
sleep 1
if curl -s http://127.0.0.1:$PORT/ > /dev/null 2>&1; then
echo "✅ Server ready (${i}s)"
break
fi
if [ $((i % 10)) -eq 0 ]; then
echo " ... ${i}s elapsed"
fi
if [ $i -eq 60 ]; then
echo ""
echo "${RED}❌ Server failed to start within 60 seconds${NC}"
echo "Log tail:"
tail -20 "$LOG_FILE"
exit 1
fi
done
echo ""
# Test function for nesting
test_nesting() {
local TEST_NUM=$1
local DESCRIPTION=$2
local PROMPT=$3
local SEED=$4
local EXPECTED_DEPTH=$5
echo "${BLUE}=== Test $TEST_NUM: $DESCRIPTION ===${NC}"
echo "Prompt: ${YELLOW}$PROMPT${NC}"
echo "Seed: $SEED"
echo "Expected nesting depth: $EXPECTED_DEPTH"
RESULT=$(curl -s -X POST http://127.0.0.1:$PORT/impact/wildcards \
-H "Content-Type: application/json" \
-d "{\"text\": \"$PROMPT\", \"seed\": $SEED}" | \
python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))" 2>/dev/null || echo "ERROR")
echo "Result: ${GREEN}$RESULT${NC}"
# Check if result contains any unexpanded wildcards
if echo "$RESULT" | grep -q "__.*__"; then
echo "Status: ${YELLOW}⚠️ WARNING - Contains unexpanded wildcards${NC}"
echo "Unexpanded: $(echo "$RESULT" | grep -o '__[^_]*__')"
elif [ "$RESULT" != "ERROR" ] && [ -n "$RESULT" ]; then
echo "Status: ${GREEN}✅ PASS - All wildcards fully expanded${NC}"
else
echo "Status: ${RED}❌ FAIL - Server error or no response${NC}"
fi
echo ""
}
echo "=========================================="
echo "Test Suite Execution"
echo "=========================================="
echo ""
# Direct level tests
echo "${CYAN}--- Direct Level Access Tests ---${NC}"
echo ""
test_nesting "01" "Level 7 (Final)" \
"__test_nesting_level7__" \
100 \
0
test_nesting "02" "Level 6 → Level 7" \
"__test_nesting_level6__" \
200 \
1
test_nesting "03" "Level 5 → Level 6 → Level 7" \
"__test_nesting_level5__" \
300 \
2
test_nesting "04" "Level 4 → ... → Level 7" \
"__test_nesting_level4__" \
400 \
3
test_nesting "05" "Level 3 → ... → Level 7" \
"__test_nesting_level3__" \
500 \
4
test_nesting "06" "Level 2 → ... → Level 7" \
"__test_nesting_level2__" \
600 \
5
test_nesting "07" "Level 1 → ... → Level 7 (Full 7 levels)" \
"__test_nesting_level1__" \
700 \
6
echo ""
echo "${CYAN}--- Multiple Nesting Tests ---${NC}"
echo ""
test_nesting "08" "Two level 1 wildcards" \
"__test_nesting_level1__ and __test_nesting_level1__" \
800 \
6
test_nesting "09" "Mixed depths" \
"__test_nesting_level1__ with __test_nesting_level4__" \
900 \
6
test_nesting "10" "Level 1 in dynamic prompt" \
"{__test_nesting_level1__|__test_nesting_level2__|__test_nesting_level3__}" \
1000 \
6
echo ""
echo "${CYAN}--- Complex Combination Tests ---${NC}"
echo ""
test_nesting "11" "Nesting with quantifier" \
"2#__test_nesting_level1__" \
1100 \
6
test_nesting "12" "Nesting with multi-select" \
"{2\$\$, \$\$__test_nesting_level1__|__test_nesting_level2__|__test_nesting_level3__}" \
1200 \
6
test_nesting "13" "Nesting with weighted selection" \
"{5::__test_nesting_level1__|3::__test_nesting_level3__|1::__test_nesting_level5__}" \
1300 \
6
test_nesting "14" "Very deep with other wildcards" \
"__test_nesting_level1__ beautiful __아름다운색__" \
1400 \
6
test_nesting "15" "All 7 levels in one prompt" \
"__test_nesting_level1__, __test_nesting_level2__, __test_nesting_level3__, __test_nesting_level4__, __test_nesting_level5__, __test_nesting_level6__, __test_nesting_level7__" \
1500 \
6
echo ""
echo "${CYAN}--- Depth-Agnostic Pattern Matching Tests ---${NC}"
echo ""
# Test 16: Depth-agnostic pattern matching with __*/test_nesting_level7__
# The __*/name__ pattern matches wildcards at ANY directory depth:
# - test_nesting_level7.txt (at root level)
# - level1/level2/.../level7/test_nesting_level7.txt (deeply nested)
# - any_folder/test_nesting_level7.txt (in any subfolder)
test_nesting "16" "Pattern matching __*/test_nesting_level7__" \
"__*/test_nesting_level7__" \
1600 \
0
# Test 17: Depth-agnostic pattern matching with __*/test_nesting_level4__
# Similar to __*/dragon__ matching both "dragon.txt" and "dragon/wizard.txt":
# - test_nesting_level4.txt (direct file)
# - level1/.../level4/test_nesting_level4.txt (nested file)
# - The pattern ignores directory depth and matches by wildcard name
test_nesting "17" "Pattern matching __*/test_nesting_level4__" \
"__*/test_nesting_level4__" \
1700 \
3
echo ""
echo "=========================================="
echo "Loaded Wildcards Check"
echo "=========================================="
# Check what wildcards were loaded
LOADED=$(curl -s http://127.0.0.1:$PORT/impact/wildcards/list/loaded 2>/dev/null | python3 -c "import sys, json; data = json.load(sys.stdin); print('\n'.join(data.get('data', [])))" 2>/dev/null || echo "ERROR")
if [ "$LOADED" != "ERROR" ]; then
echo "Loaded wildcards:"
echo "$LOADED" | grep -E "test_nesting" | sed 's/^/ /'
NESTING_COUNT=$(echo "$LOADED" | grep -c "test_nesting" || echo "0")
echo ""
echo "Total nesting wildcards loaded: $NESTING_COUNT"
if [ "$NESTING_COUNT" -ge 7 ]; then
echo "${GREEN}✅ All 7 nesting levels loaded${NC}"
else
echo "${YELLOW}⚠️ Only $NESTING_COUNT nesting levels loaded (expected 7)${NC}"
fi
else
echo "${YELLOW}⚠️ Could not retrieve loaded wildcards list${NC}"
fi
echo ""
echo "=========================================="
echo "Summary"
echo "=========================================="
echo "${GREEN}✅ Deep nesting tests completed${NC}"
echo ""
echo "Test results:"
echo " 1. 7-level transitive expansion tested ✓"
echo " 2. All depth levels (1-7) individually tested ✓"
echo " 3. Mixed depth combinations tested ✓"
echo " 4. Nesting with quantifiers and multi-select ✓"
echo " 5. Nesting with weighted selection ✓"
echo " 6. Depth-agnostic pattern matching (__*/pattern__) ✓"
echo " 7. Complex multi-wildcard prompts ✓"
echo ""
echo "Maximum nesting depth verified: 7 levels"
echo "All wildcards should be fully expanded without crashes"
echo ""
echo "Log file: $LOG_FILE"

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#!/bin/bash
# Comprehensive Dynamic Prompt Validation Test
# Tests all dynamic prompt features with statistical validation
PORT=8188
GREEN='\033[0;32m'
RED='\033[0;31m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m'
LOG_FILE="/tmp/dynamic_prompt_full_validation.log"
exec > >(tee -a "$LOG_FILE")
exec 2>&1
echo "=========================================="
echo "Dynamic Prompt Full Validation Test"
echo "=========================================="
echo "Validating: All dynamic prompt features"
echo ""
# Check server
if ! curl -s http://127.0.0.1:$PORT/ > /dev/null 2>&1; then
echo "${RED}Server not running on port $PORT${NC}"
echo "Start server with: cd /mnt/teratera/git/ComfyUI && bash run.sh --listen 127.0.0.1 --port $PORT"
exit 1
fi
TOTAL_GROUPS=0
PASSED_GROUPS=0
FAILED_GROUPS=0
# Test function for multiselect with validation
test_multiselect() {
local TEST_NAME=$1
local PROMPT=$2
local EXPECTED_COUNT=$3
local SEPARATOR=$4
local ITERATIONS=$5
shift 5
local OPTIONS=("$@")
echo "${BLUE}=== $TEST_NAME ===${NC}"
echo "Prompt: ${YELLOW}$PROMPT${NC}"
echo "Expected: $EXPECTED_COUNT items per result, separator: '$SEPARATOR'"
echo -n "Testing $ITERATIONS iterations: "
local PASSED=0
local FAILED=0
declare -a FAILURES
for i in $(seq 1 $ITERATIONS); do
SEED=$((1000 + i * 100))
RESULT=$(curl -s -X POST http://127.0.0.1:$PORT/impact/wildcards \
-H "Content-Type: application/json" \
-d "{\"text\": \"$PROMPT\", \"seed\": $SEED}" | \
python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))" 2>/dev/null || echo "ERROR")
if [ "$RESULT" = "ERROR" ]; then
echo -n "X"
((FAILED++))
FAILURES+=(" Iteration $i (seed $SEED): Server error")
continue
fi
# Count items based on separator
if [ -z "$SEPARATOR" ]; then
ITEM_COUNT=1
else
ITEM_COUNT=$(echo "$RESULT" | awk -F"$SEPARATOR" '{print NF}')
fi
# Check if count matches
if [ $ITEM_COUNT -ne $EXPECTED_COUNT ]; then
echo -n "X"
((FAILED++))
FAILURES+=(" Iteration $i (seed $SEED): Expected $EXPECTED_COUNT items, got $ITEM_COUNT" " Result: $RESULT")
continue
fi
# Check for duplicates (split by separator and check uniqueness)
if [ -n "$SEPARATOR" ]; then
UNIQUE_COUNT=$(echo "$RESULT" | awk -F"$SEPARATOR" '{for(i=1;i<=NF;i++) print $i}' | sort -u | wc -l)
if [ $UNIQUE_COUNT -ne $EXPECTED_COUNT ]; then
echo -n "D"
((FAILED++))
FAILURES+=(" Iteration $i (seed $SEED): Duplicates detected" " Result: $RESULT")
continue
fi
fi
# Check that all items are from the option list
VALID=1
if [ -n "$SEPARATOR" ]; then
while IFS= read -r item; do
item=$(echo "$item" | xargs) # trim whitespace
FOUND=0
for opt in "${OPTIONS[@]}"; do
if [ "$item" = "$opt" ]; then
FOUND=1
break
fi
done
if [ $FOUND -eq 0 ]; then
VALID=0
break
fi
done < <(echo "$RESULT" | awk -F"$SEPARATOR" '{for(i=1;i<=NF;i++) print $i}')
fi
if [ $VALID -eq 0 ]; then
echo -n "?"
((FAILED++))
FAILURES+=(" Iteration $i (seed $SEED): Invalid items detected" " Result: $RESULT")
continue
fi
echo -n "."
((PASSED++))
done
echo " Done"
echo "Results: ${GREEN}$PASSED passed${NC}, ${RED}$FAILED failed${NC}"
if [ $FAILED -gt 0 ]; then
echo -e "${RED}Failures:${NC}"
printf '%s\n' "${FAILURES[@]}"
((FAILED_GROUPS++))
else
echo "${GREEN}✅ PASS${NC}"
((PASSED_GROUPS++))
fi
echo ""
((TOTAL_GROUPS++))
}
# Test function for weighted selection with statistical validation
test_weighted() {
local TEST_NAME=$1
local PROMPT=$2
local ITERATIONS=$3
shift 3
local OPTIONS=("$@")
echo "${BLUE}=== $TEST_NAME ===${NC}"
echo "Prompt: ${YELLOW}$PROMPT${NC}"
echo -n "Testing $ITERATIONS iterations: "
declare -A COUNTS
local TOTAL=0
for i in $(seq 1 $ITERATIONS); do
SEED=$((1000 + i * 100))
RESULT=$(curl -s -X POST http://127.0.0.1:$PORT/impact/wildcards \
-H "Content-Type: application/json" \
-d "{\"text\": \"$PROMPT\", \"seed\": $SEED}" | \
python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))" 2>/dev/null || echo "ERROR")
if [ "$RESULT" = "ERROR" ]; then
echo -n "X"
continue
fi
MATCHED=0
for opt in "${OPTIONS[@]}"; do
if echo "$RESULT" | grep -Fq "$opt"; then
COUNTS[$opt]=$((${COUNTS[$opt]:-0} + 1))
MATCHED=1
break
fi
done
if [ $MATCHED -eq 1 ]; then
((TOTAL++))
echo -n "."
else
echo -n "?"
fi
done
echo " Done"
echo "Distribution:"
for opt in "${OPTIONS[@]}"; do
local COUNT=${COUNTS[$opt]:-0}
local PERCENT=0
if [ $TOTAL -gt 0 ]; then
PERCENT=$(awk "BEGIN {printf \"%.1f\", ($COUNT / $TOTAL) * 100}")
fi
echo " $opt: $COUNT / $TOTAL (${PERCENT}%)"
done
echo "${GREEN}✅ PASS${NC}"
((PASSED_GROUPS++))
((TOTAL_GROUPS++))
echo ""
}
echo "=========================================="
echo "MULTISELECT VALIDATION"
echo "=========================================="
echo ""
test_multiselect "Test 1: 2-item multiselect" "{2\$\$, \$\$red|blue|green|yellow}" 2 ", " 20 "red" "blue" "green" "yellow"
test_multiselect "Test 2: 3-item multiselect" "{3\$\$ and \$\$alpha|beta|gamma|delta|epsilon}" 3 " and " 20 "alpha" "beta" "gamma" "delta" "epsilon"
test_multiselect "Test 3: Single-item multiselect" "{1\$\$ \$\$one|two|three}" 1 " " 20 "one" "two" "three"
test_multiselect "Test 4: Max-item multiselect (all 4)" "{4\$\$-\$\$cat|dog|bird|fish}" 4 "-" 20 "cat" "dog" "bird" "fish"
echo "=========================================="
echo "WEIGHTED SELECTION VALIDATION"
echo "=========================================="
echo ""
test_weighted "Test 5: Heavy bias 10:1 (100 iterations)" "{10::common|1::rare}" 100 "common" "rare"
test_weighted "Test 6: Equal weights 1:1:1 (60 iterations)" "{1::alpha|1::beta|1::gamma}" 60 "alpha" "beta" "gamma"
test_weighted "Test 7: Extreme bias 100:1 (100 iterations)" "{100::very_common|1::very_rare}" 100 "very_common" "very_rare"
test_weighted "Test 8: Multi-level weights 5:3:2 (100 iterations)" "{5::high|3::medium|2::low}" 100 "high" "medium" "low"
test_weighted "Test 9: Default weight mixing (100 iterations)" "{10::weighted|unweighted}" 100 "weighted" "unweighted"
echo "=========================================="
echo "BASIC SELECTION VALIDATION"
echo "=========================================="
echo ""
test_weighted "Test 10: Simple random selection (50 iterations)" "{option_a|option_b|option_c}" 50 "option_a" "option_b" "option_c"
test_weighted "Test 11: Nested selection (50 iterations)" "{outer_{inner1|inner2}|simple}" 50 "outer_inner1" "outer_inner2" "simple"
echo "=========================================="
echo "SUMMARY"
echo "=========================================="
echo ""
echo "Total test groups: $TOTAL_GROUPS"
echo "${GREEN}Passed: $PASSED_GROUPS${NC}"
echo "${RED}Failed: $FAILED_GROUPS${NC}"
echo ""
if [ $FAILED_GROUPS -eq 0 ]; then
echo "${GREEN}✅ All tests passed${NC}"
exit 0
else
echo "${RED}❌ Some tests failed${NC}"
exit 1
fi

225
custom_nodes/ComfyUI-Impact-Pack/tests/test_edge_cases.sh Executable file
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#!/bin/bash
# Edge Cases Test Suite
# Tests edge cases: empty lines, whitespace, long lines, special characters, etc.
set -e
PORT=8196
COMFYUI_DIR="/mnt/teratera/git/ComfyUI"
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMPACT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
CONFIG_FILE="$IMPACT_DIR/impact-pack.ini"
LOG_FILE="/tmp/edge_cases_test.log"
GREEN='\033[0;32m'
RED='\033[0;31m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m'
echo "=========================================="
echo "Edge Cases Test Suite"
echo "=========================================="
echo "Port: $PORT"
echo "Testing: Edge cases and boundary conditions"
echo ""
# Cleanup function
cleanup() {
echo ""
echo "Cleaning up..."
pkill -f "python.*main.py.*--port $PORT" 2>/dev/null || true
rm -f "$CONFIG_FILE"
echo "Cleanup complete"
}
trap cleanup EXIT
# Kill any existing server on this port
echo "Killing any existing server on port $PORT..."
pkill -f "python.*main.py.*--port $PORT" 2>/dev/null || true
sleep 2
# Setup configuration
echo "Setting up configuration..."
cat > "$CONFIG_FILE" << EOF
[default]
custom_wildcards = $IMPACT_DIR/tests/wildcards/samples
wildcard_cache_limit_mb = 50
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
disable_gpu_opencv = True
EOF
echo "Configuration created: custom_wildcards = $IMPACT_DIR/tests/wildcards/samples"
echo ""
# Start server
echo "Starting ComfyUI server on port $PORT..."
cd "$COMFYUI_DIR"
bash run.sh --listen 127.0.0.1 --port $PORT > "$LOG_FILE" 2>&1 &
SERVER_PID=$!
echo "Server PID: $SERVER_PID"
# Wait for server startup
echo "Waiting for server startup..."
for i in {1..60}; do
sleep 1
if curl -s http://127.0.0.1:$PORT/ > /dev/null 2>&1; then
echo "✅ Server ready (${i}s)"
break
fi
if [ $((i % 10)) -eq 0 ]; then
echo " ... ${i}s elapsed"
fi
if [ $i -eq 60 ]; then
echo ""
echo "${RED}❌ Server failed to start within 60 seconds${NC}"
echo "Log tail:"
tail -20 "$LOG_FILE"
exit 1
fi
done
echo ""
# Test function
test_edge_case() {
local TEST_NUM=$1
local DESCRIPTION=$2
local PROMPT=$3
local SEED=$4
echo "${BLUE}=== Test $TEST_NUM: $DESCRIPTION ===${NC}"
echo "Prompt: ${YELLOW}$PROMPT${NC}"
echo "Seed: $SEED"
RESULT=$(curl -s -X POST http://127.0.0.1:$PORT/impact/wildcards \
-H "Content-Type: application/json" \
-d "{\"text\": \"$PROMPT\", \"seed\": $SEED}" | \
python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))" 2>/dev/null || echo "ERROR")
echo "Result: ${GREEN}$RESULT${NC}"
if [ "$RESULT" != "ERROR" ] && [ -n "$RESULT" ]; then
echo "Status: ${GREEN}✅ PASS${NC}"
else
echo "Status: ${RED}❌ FAIL${NC}"
fi
echo ""
}
echo "=========================================="
echo "Test Suite Execution"
echo "=========================================="
echo ""
# Empty Lines and Whitespace Tests
test_edge_case "01" "Empty lines handling" \
"__test_edge_empty_lines__" \
100
test_edge_case "02" "Whitespace handling" \
"__test_edge_whitespace__" \
200
test_edge_case "03" "Long lines handling" \
"__test_edge_long_lines__" \
300
# Special Characters Tests
test_edge_case "04" "Special characters in content" \
"__test_edge_special_chars__" \
400
test_edge_case "05" "Embedded wildcard syntax" \
"__test_edge_special_chars__" \
401
# Case Insensitivity Tests
test_edge_case "06" "Lowercase wildcard" \
"__test_edge_case_insensitive__" \
500
test_edge_case "07" "UPPERCASE wildcard" \
"__TEST_EDGE_CASE_INSENSITIVE__" \
500
test_edge_case "08" "MixedCase wildcard" \
"__TeSt_EdGe_CaSe_InSeNsItIvE__" \
500
# Comment Handling Tests
test_edge_case "09" "Comments in wildcard file" \
"__test_comments__" \
600
# Pattern Matching Tests
test_edge_case "10" "Pattern matching __*/name__" \
"__*/test_pattern_match__" \
700
test_edge_case "11" "Direct pattern match" \
"__test_pattern_match__" \
700
# Quantifier Tests
test_edge_case "12" "Quantifier 3#" \
"3#__test_quantifier__" \
800
test_edge_case "13" "Quantifier 5# with dynamic" \
"{2\$\$, \$\$5#__test_quantifier__}" \
801
# Complex Combinations
test_edge_case "14" "Mixed special chars and wildcards" \
"__test_edge_special_chars__ with {option1|option2}" \
900
test_edge_case "15" "Long prompt with multiple wildcards" \
"__test_edge_empty_lines__ and __test_edge_whitespace__ and __test_comments__" \
1000
# Boundary Conditions
test_edge_case "16" "Very long dynamic prompt" \
"{__test_edge_long_lines__|__test_edge_whitespace__|__test_edge_empty_lines__|__test_comments__|__test_edge_special_chars__}" \
1100
test_edge_case "17" "Nested wildcards in dynamic" \
"{red __test_quantifier__|blue __test_pattern_match__|green __test_comments__}" \
1200
test_edge_case "18" "Quantifier with case-insensitive" \
"2#__TEST_QUANTIFIER__" \
1300
# Stress Tests
test_edge_case "19" "Multiple quantifiers" \
"3#__test_quantifier__ and 2#__test_comments__" \
1400
test_edge_case "20" "Case insensitive pattern match" \
"__*/TEST_PATTERN_MATCH__" \
1500
echo ""
echo "=========================================="
echo "Summary"
echo "=========================================="
echo "${GREEN}✅ Edge case tests completed${NC}"
echo ""
echo "All tests verified edge case handling:"
echo " 1. Empty lines and whitespace ✓"
echo " 2. Very long lines ✓"
echo " 3. Special characters ✓"
echo " 4. Case-insensitive matching ✓"
echo " 5. Comment line filtering ✓"
echo " 6. Pattern matching (__*/name__) ✓"
echo " 7. Quantifiers (N#__wildcard__) ✓"
echo " 8. Complex combinations ✓"
echo " 9. Boundary conditions ✓"
echo ""
echo "Log file: $LOG_FILE"

204
custom_nodes/ComfyUI-Impact-Pack/tests/test_encoding.sh Executable file
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#!/bin/bash
# UTF-8 Encoding Test Suite
# Tests multi-language support (Korean, Chinese, Arabic, emoji)
set -e
PORT=8198
COMFYUI_DIR="/mnt/teratera/git/ComfyUI"
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMPACT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
CONFIG_FILE="$IMPACT_DIR/impact-pack.ini"
LOG_FILE="/tmp/encoding_test.log"
GREEN='\033[0;32m'
RED='\033[0;31m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m'
echo "=========================================="
echo "UTF-8 Encoding Test Suite"
echo "=========================================="
echo "Port: $PORT"
echo "Testing: Multi-language encoding support"
echo ""
# Cleanup function
cleanup() {
echo ""
echo "Cleaning up..."
pkill -f "python.*main.py.*--port $PORT" 2>/dev/null || true
rm -f "$CONFIG_FILE"
echo "Cleanup complete"
}
trap cleanup EXIT
# Kill any existing server on this port
echo "Killing any existing server on port $PORT..."
pkill -f "python.*main.py.*--port $PORT" 2>/dev/null || true
sleep 2
# Setup configuration
echo "Setting up configuration..."
cat > "$CONFIG_FILE" << EOF
[default]
custom_wildcards = $IMPACT_DIR/tests/wildcards/samples
wildcard_cache_limit_mb = 50
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
disable_gpu_opencv = True
EOF
echo "Configuration created: custom_wildcards = $IMPACT_DIR/tests/wildcards/samples"
echo ""
# Start server
echo "Starting ComfyUI server on port $PORT..."
cd "$COMFYUI_DIR"
bash run.sh --listen 127.0.0.1 --port $PORT > "$LOG_FILE" 2>&1 &
SERVER_PID=$!
echo "Server PID: $SERVER_PID"
# Wait for server startup
echo "Waiting for server startup..."
for i in {1..60}; do
sleep 1
if curl -s http://127.0.0.1:$PORT/ > /dev/null 2>&1; then
echo "✅ Server ready (${i}s)"
break
fi
if [ $((i % 10)) -eq 0 ]; then
echo " ... ${i}s elapsed"
fi
if [ $i -eq 60 ]; then
echo ""
echo "${RED}❌ Server failed to start within 60 seconds${NC}"
echo "Log tail:"
tail -20 "$LOG_FILE"
exit 1
fi
done
echo ""
# Test function
test_encoding() {
local TEST_NUM=$1
local DESCRIPTION=$2
local PROMPT=$3
local SEED=$4
echo "${BLUE}=== Test $TEST_NUM: $DESCRIPTION ===${NC}"
echo "Prompt: ${YELLOW}$PROMPT${NC}"
echo "Seed: $SEED"
RESULT=$(curl -s -X POST http://127.0.0.1:$PORT/impact/wildcards \
-H "Content-Type: application/json" \
-d "{\"text\": \"$PROMPT\", \"seed\": $SEED}" | \
python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))" 2>/dev/null || echo "ERROR")
echo "Result: ${GREEN}$RESULT${NC}"
# Check if result contains non-ASCII characters (UTF-8)
if echo "$RESULT" | grep -qP '[\x80-\xFF]'; then
echo "Status: ${GREEN}✅ PASS - UTF-8 characters preserved${NC}"
elif [ "$RESULT" != "ERROR" ] && [ -n "$RESULT" ]; then
echo "Status: ${YELLOW}⚠️ WARNING - No UTF-8 characters in result${NC}"
else
echo "Status: ${RED}❌ FAIL - Server error or no response${NC}"
fi
echo ""
}
echo "=========================================="
echo "Test Suite Execution"
echo "=========================================="
echo ""
# Korean Tests (K-pop theme with Korean filename)
test_encoding "01" "Korean Hangul (아름다운색)" \
"__아름다운색__" \
100
test_encoding "02" "Korean with emoji" \
"🌸 __아름다운색__" \
200
test_encoding "03" "Korean in dynamic prompt" \
"{붉은|하얀|노란} __아름다운색__" \
300
# Emoji Tests
test_encoding "04" "Emoji wildcard" \
"__test_encoding_emoji__" \
400
test_encoding "05" "Multiple emojis" \
"🌸 beautiful 🌺 garden 🌼" \
500
test_encoding "06" "Emoji in dynamic prompt" \
"{🌸|🌺|🌼|🌻|🌷}" \
600
# Special Characters Tests
test_encoding "07" "Mathematical symbols" \
"__test_encoding_special__" \
700
test_encoding "08" "Currency symbols" \
"Price: {$|€|£|¥|₩} 100" \
800
# Mixed Language Tests
test_encoding "09" "Korean + Chinese" \
"아름다운 __아름다운색__" \
900
test_encoding "10" "Korean + Emoji + English" \
"🌸 beautiful 아름다운 __아름다운색__" \
1000
# RTL (Right-to-Left) Tests
test_encoding "11" "Arabic RTL text" \
"زهرة جميلة" \
1100
# Edge Cases
test_encoding "12" "Korean in quantifier (아름다운색)" \
"3#__아름다운색__" \
1200
test_encoding "13" "Korean in multi-select (아름다운색)" \
"{2\$\$, \$\$__아름다운색__|장미|벚꽃}" \
1300
test_encoding "14" "Mixed UTF-8 in weighted selection" \
"{5::🌸|3::장미|2::花}" \
1400
test_encoding "15" "Very long Korean text (아름다운색)" \
"아름다운 {붉은|하얀|노란|분홍|보라} __아름다운색__ 꽃밭에서" \
1500
echo ""
echo "=========================================="
echo "Summary"
echo "=========================================="
echo "${GREEN}✅ Encoding tests completed${NC}"
echo ""
echo "All tests verified UTF-8 encoding support:"
echo " 1. Korean (Hangul) characters ✓"
echo " 2. Emoji support ✓"
echo " 3. Chinese characters ✓"
echo " 4. Arabic (RTL) text ✓"
echo " 5. Mathematical and special symbols ✓"
echo " 6. Mixed multi-language content ✓"
echo " 7. UTF-8 in dynamic prompts ✓"
echo " 8. UTF-8 with quantifiers and multi-select ✓"
echo ""
echo "Log file: $LOG_FILE"

195
custom_nodes/ComfyUI-Impact-Pack/tests/test_error_handling.sh Executable file
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#!/bin/bash
# Error Handling Test Suite
# Tests graceful error handling for invalid wildcards, circular references, etc.
set -e
PORT=8197
COMFYUI_DIR="/mnt/teratera/git/ComfyUI"
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMPACT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
CONFIG_FILE="$IMPACT_DIR/impact-pack.ini"
LOG_FILE="/tmp/error_handling_test.log"
GREEN='\033[0;32m'
RED='\033[0;31m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m'
echo "=========================================="
echo "Error Handling Test Suite"
echo "=========================================="
echo "Port: $PORT"
echo "Testing: Error handling and edge cases"
echo ""
# Cleanup function
cleanup() {
echo ""
echo "Cleaning up..."
pkill -f "python.*main.py.*--port $PORT" 2>/dev/null || true
rm -f "$CONFIG_FILE"
echo "Cleanup complete"
}
trap cleanup EXIT
# Kill any existing server on this port
echo "Killing any existing server on port $PORT..."
pkill -f "python.*main.py.*--port $PORT" 2>/dev/null || true
sleep 2
# Setup configuration to use test wildcard samples
echo "Setting up configuration..."
cat > "$CONFIG_FILE" << EOF
[default]
custom_wildcards = $IMPACT_DIR/tests/wildcards/samples
wildcard_cache_limit_mb = 50
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
disable_gpu_opencv = True
EOF
echo "Configuration created: custom_wildcards = $IMPACT_DIR/tests/wildcards/samples"
echo ""
# Start server
echo "Starting ComfyUI server on port $PORT..."
cd "$COMFYUI_DIR"
bash run.sh --listen 127.0.0.1 --port $PORT > "$LOG_FILE" 2>&1 &
SERVER_PID=$!
echo "Server PID: $SERVER_PID"
# Wait for server startup
echo "Waiting for server startup..."
for i in {1..60}; do
sleep 1
if curl -s http://127.0.0.1:$PORT/ > /dev/null 2>&1; then
echo "✅ Server ready (${i}s)"
break
fi
if [ $((i % 10)) -eq 0 ]; then
echo " ... ${i}s elapsed"
fi
if [ $i -eq 60 ]; then
echo ""
echo "${RED}❌ Server failed to start within 60 seconds${NC}"
echo "Log tail:"
tail -20 "$LOG_FILE"
exit 1
fi
done
echo ""
# Test function
test_error_case() {
local TEST_NUM=$1
local DESCRIPTION=$2
local PROMPT=$3
local SEED=$4
local EXPECTED_BEHAVIOR=$5
echo "${BLUE}=== Test $TEST_NUM: $DESCRIPTION ===${NC}"
echo "Prompt: ${YELLOW}$PROMPT${NC}"
echo "Seed: $SEED"
echo "Expected: $EXPECTED_BEHAVIOR"
RESULT=$(curl -s -X POST http://127.0.0.1:$PORT/impact/wildcards \
-H "Content-Type: application/json" \
-d "{\"text\": \"$PROMPT\", \"seed\": $SEED}" | \
python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))" 2>/dev/null || echo "ERROR")
echo "Result: ${GREEN}$RESULT${NC}"
# Check if result is not an error
if [ "$RESULT" != "ERROR" ] && [ -n "$RESULT" ]; then
echo "Status: ${GREEN}✅ PASS - No crash, graceful handling${NC}"
else
echo "Status: ${RED}❌ FAIL - Server error or no response${NC}"
fi
echo ""
}
echo "=========================================="
echo "Test Suite Execution"
echo "=========================================="
echo ""
# Test 1: Non-existent wildcard reference
test_error_case "01" "Non-existent wildcard" \
"__test_error_cases__" \
42 \
"Should handle missing wildcard gracefully"
# Test 2: Circular reference detection
test_error_case "02" "Circular reference A" \
"__test_circular_a__" \
100 \
"Should detect cycle and stop at max iterations"
# Test 3: Circular reference from B
test_error_case "03" "Circular reference B" \
"__test_circular_b__" \
200 \
"Should detect cycle and stop at max iterations"
# Test 4: Completely non-existent wildcard
test_error_case "04" "Completely missing wildcard" \
"__this_file_does_not_exist__" \
42 \
"Should leave unexpanded or show error"
# Test 5: Mixed valid and invalid
test_error_case "05" "Mixed valid and invalid" \
"beautiful __test_quantifier__ with __nonexistent__" \
42 \
"Should expand valid, handle invalid gracefully"
# Test 6: Empty dynamic prompt
test_error_case "06" "Empty dynamic option" \
"{|something|nothing}" \
42 \
"Should handle empty option"
# Test 7: Single option dynamic
test_error_case "07" "Single option dynamic" \
"{only_one}" \
42 \
"Should return the single option"
# Test 8: Malformed dynamic prompt (unclosed)
test_error_case "08" "Malformed dynamic prompt" \
"{option1|option2" \
42 \
"Should handle unclosed bracket gracefully"
# Test 9: Very deeply nested dynamic prompts
test_error_case "09" "Very deep nesting" \
"{a|{b|{c|{d|{e|{f|{g|{h|i}}}}}}}" \
42 \
"Should handle deep nesting without crash"
# Test 10: Multiple circular references in one prompt
test_error_case "10" "Multiple circular refs" \
"__test_circular_a__ and __test_circular_b__" \
42 \
"Should handle multiple circular references"
echo ""
echo "=========================================="
echo "Summary"
echo "=========================================="
echo "${GREEN}✅ Error handling tests completed${NC}"
echo ""
echo "All tests verified graceful error handling:"
echo " 1. Non-existent wildcards handled"
echo " 2. Circular references detected (max 100 iterations)"
echo " 3. Malformed syntax handled gracefully"
echo " 4. Deep nesting processed correctly"
echo " 5. No server crashes occurred"
echo ""
echo "Log file: $LOG_FILE"

228
custom_nodes/ComfyUI-Impact-Pack/tests/test_ondemand_loading.sh Executable file
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#!/bin/bash
# On-Demand Lazy Loading Test Suite
# Tests progressive on-demand wildcard loading with cache limits
set -e
PORT=8193
COMFYUI_DIR="/mnt/teratera/git/ComfyUI"
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMPACT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
CONFIG_FILE="$IMPACT_DIR/impact-pack.ini"
LOG_FILE="/tmp/ondemand_test.log"
TEMP_SAMPLES_DIR="/tmp/ondemand_test_samples"
GREEN='\033[0;32m'
RED='\033[0;31m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m'
echo "=========================================="
echo "On-Demand Lazy Loading Test Suite"
echo "=========================================="
echo "Port: $PORT"
echo "Testing: Progressive on-demand wildcard loading"
echo ""
# Cleanup function
cleanup() {
echo ""
echo "Cleaning up..."
pkill -f "python.*main.py.*--port $PORT" 2>/dev/null || true
rm -f "$CONFIG_FILE"
rm -rf "$TEMP_SAMPLES_DIR"
echo "Cleanup complete"
}
trap cleanup EXIT
# Create temporary sample files for on-demand testing
echo "Creating temporary sample files..."
mkdir -p "$TEMP_SAMPLES_DIR"
# Create large sample files to test cache limits
for i in {1..50}; do
cat > "$TEMP_SAMPLES_DIR/large_sample_${i}.txt" << EOF
# Large sample file $i for on-demand loading test
$(for j in {1..100}; do echo "option_${i}_${j}"; done)
EOF
done
# Create Korean sample
cp "$SCRIPT_DIR/wildcards/samples/아름다운색.txt" "$TEMP_SAMPLES_DIR/" 2>/dev/null || \
cat > "$TEMP_SAMPLES_DIR/아름다운색.txt" << 'EOF'
수놓은 별빛
벚꽃 핑크
강코랄
옌로우
챈메랄드
챔무
백설민주
나부키하늘
토미베이지
율렌지
블루지니
캔디핑크
EOF
# Create nesting samples
mkdir -p "$TEMP_SAMPLES_DIR/level1/level2/level3"
echo "__large_sample_10__" > "$TEMP_SAMPLES_DIR/level1/test_nesting_level1.txt"
echo "option_a" >> "$TEMP_SAMPLES_DIR/level1/test_nesting_level1.txt"
echo "__large_sample_20__" > "$TEMP_SAMPLES_DIR/level1/level2/test_nesting_level2.txt"
echo "option_b" >> "$TEMP_SAMPLES_DIR/level1/level2/test_nesting_level2.txt"
echo "final_option" > "$TEMP_SAMPLES_DIR/level1/level2/level3/test_nesting_level3.txt"
echo "✅ Created $(find $TEMP_SAMPLES_DIR -name '*.txt' | wc -l) temporary sample files"
echo ""
# Kill any existing server on this port
echo "Killing any existing server on port $PORT..."
pkill -f "python.*main.py.*--port $PORT" 2>/dev/null || true
sleep 2
# Test function for on-demand mode
test_ondemand() {
local TEST_NUM=$1
local DESCRIPTION=$2
local CACHE_LIMIT=$3
local PROMPT=$4
local SEED=$5
echo "${BLUE}=== Test $TEST_NUM: $DESCRIPTION ===${NC}"
echo "Cache Limit: ${YELLOW}${CACHE_LIMIT}MB${NC}"
echo "Prompt: ${YELLOW}$PROMPT${NC}"
echo "Seed: $SEED"
# Restart server with new cache limit
pkill -f "python.*main.py.*--port $PORT" 2>/dev/null || true
sleep 2
# Setup configuration with cache limit pointing to temporary samples
cat > "$CONFIG_FILE" << EOF
[default]
custom_wildcards = $TEMP_SAMPLES_DIR
wildcard_cache_limit_mb = $CACHE_LIMIT
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
disable_gpu_opencv = True
EOF
# Start server
cd "$COMFYUI_DIR"
bash run.sh --listen 127.0.0.1 --port $PORT > "$LOG_FILE" 2>&1 &
SERVER_PID=$!
# Wait for server
for i in {1..60}; do
sleep 1
if curl -s http://127.0.0.1:$PORT/ > /dev/null 2>&1; then
break
fi
if [ $i -eq 60 ]; then
echo "${RED}❌ Server failed to start${NC}"
exit 1
fi
done
# Test wildcard expansion
RESULT=$(curl -s -X POST http://127.0.0.1:$PORT/impact/wildcards \
-H "Content-Type: application/json" \
-d "{\"text\": \"$PROMPT\", \"seed\": $SEED}" | \
python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))" 2>/dev/null || echo "ERROR")
echo "Result: ${GREEN}$RESULT${NC}"
# Get loaded wildcards count
LOADED_COUNT=$(curl -s http://127.0.0.1:$PORT/impact/wildcards/list/loaded 2>/dev/null | \
python3 -c "import sys, json; print(len(json.load(sys.stdin).get('data',[])))" 2>/dev/null || echo "0")
echo "Loaded wildcards: ${YELLOW}$LOADED_COUNT${NC}"
if [ "$RESULT" != "ERROR" ] && [ -n "$RESULT" ]; then
echo "Status: ${GREEN}✅ PASS - On-demand loading working${NC}"
else
echo "Status: ${RED}❌ FAIL - Server error${NC}"
fi
echo ""
}
echo "=========================================="
echo "Test Suite Execution"
echo "=========================================="
echo ""
# Test 1: Small cache limit (1MB) - should enable on-demand mode
test_ondemand "01" "Small cache limit (1MB) - on-demand enabled" \
"1" \
"__아름다운색__" \
100
# Test 2: Moderate cache limit (10MB) - on-demand mode
test_ondemand "02" "Moderate cache limit (10MB) - progressive loading" \
"10" \
"__large_sample_5__" \
200
# Test 3: Large cache limit (100MB) - eager loading
test_ondemand "03" "Large cache limit (100MB) - eager loading" \
"100" \
"__아름다운색__" \
300
# Test 4: Very small cache (0.5MB) - aggressive lazy loading
test_ondemand "04" "Very small cache (0.5MB) - aggressive lazy loading" \
"0.5" \
"{__아름다운색__|__large_sample_15__|__large_sample_25__}" \
400
# Test 5: Default cache (50MB) - balanced mode
test_ondemand "05" "Default cache (50MB) - balanced mode" \
"50" \
"2#__large_sample_30__" \
500
# Test 6: On-demand with deep nesting
test_ondemand "06" "On-demand with 3-level nesting (5MB cache)" \
"5" \
"__level1/test_nesting_level1__" \
600
# Test 7: On-demand with multiple wildcards
test_ondemand "07" "On-demand with multiple wildcards (2MB cache)" \
"2" \
"__아름다운색__ and __large_sample_1__ in {__large_sample_40__|__large_sample_45__}" \
700
# Test 8: Cache limit boundary test
test_ondemand "08" "Cache boundary - exactly at limit (25MB)" \
"25" \
"{2$$,$$__large_sample_10__|__large_sample_20__|__large_sample_30__}" \
800
echo ""
echo "=========================================="
echo "Summary"
echo "=========================================="
echo "${GREEN}✅ On-demand loading tests completed${NC}"
echo ""
echo "Test results:"
echo " 1. Small cache (1MB) - on-demand enabled ✓"
echo " 2. Moderate cache (10MB) - progressive loading ✓"
echo " 3. Large cache (100MB) - eager loading ✓"
echo " 4. Aggressive lazy loading (0.5MB) ✓"
echo " 5. Balanced mode (50MB default) ✓"
echo " 6. On-demand with deep nesting ✓"
echo " 7. On-demand with multiple wildcards ✓"
echo " 8. Cache boundary testing ✓"
echo ""
echo "On-demand mode verification:"
echo " - LazyWildcardLoader initialization ✓"
echo " - Progressive data loading ✓"
echo " - Memory-efficient operation ✓"
echo " - Cache limit enforcement ✓"
echo ""
echo "Log file: $LOG_FILE"

961
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@@ -0,0 +1,961 @@
# Wildcard System - Complete Test Suite
Comprehensive testing guide for the ComfyUI Impact Pack wildcard system.
---
## 📋 Quick Links
- **[Quick Start](#quick-start)** - Run tests in 5 minutes
- **[Test Categories](#test-categories)** - All test types
- **[Test Execution](#test-execution)** - How to run each test
- **[Troubleshooting](#troubleshooting)** - Common issues
---
## Overview
### Test Suite Structure
```
tests/
├── wildcards/ # Wildcard system tests
│ ├── Unit Tests (Python)
│ │ ├── test_wildcard_lazy_loading.py # LazyWildcardLoader class
│ │ ├── test_progressive_loading.py # Progressive loading
│ │ ├── test_wildcard_final.py # Final validation
│ │ └── test_lazy_load_verification.py # Lazy load verification
│ │
│ ├── Integration Tests (Shell + API)
│ │ ├── test_progressive_ondemand.sh # ⭐ Progressive loading (NEW)
│ │ ├── test_lazy_load_api.sh # Lazy loading consistency
│ │ ├── test_sequential_loading.sh # Transitive wildcards
│ │ ├── test_versatile_prompts.sh # Feature tests
│ │ ├── test_wildcard_consistency.sh # Consistency validation
│ │ └── test_wildcard_features.sh # Core features
│ │
│ ├── Utility Scripts
│ │ ├── find_transitive_wildcards.sh # Find transitive chains
│ │ ├── find_deep_transitive.py # Deep transitive analysis
│ │ ├── verify_ondemand_mode.sh # Verify on-demand activation
│ │ └── run_quick_test.sh # Quick validation
│ │
│ └── README.md (this file)
│
└── workflows/ # Workflow test files
├── advanced-sampler.json
├── detailer-pipe-test.json
└── ...
```
### Test Coverage
- **11 test files** (4 Python, 7 Shell)
- **100+ test scenarios**
- **~95% feature coverage**
- **~15 minutes** total execution time
---
## Quick Start
### Run All Tests
```bash
cd /path/to/ComfyUI/custom_nodes/comfyui-impact-pack/tests/wildcards
# Run all shell tests
for test in test_*.sh; do
echo "Running: $test"
bash "$test"
done
```
### Run Specific Test
```bash
cd /path/to/ComfyUI/custom_nodes/comfyui-impact-pack/tests/wildcards
# Progressive loading (NEW)
bash test_progressive_ondemand.sh
# Lazy loading
bash test_lazy_load_api.sh
# Sequential/transitive
bash test_sequential_loading.sh
# Versatile prompts
bash test_versatile_prompts.sh
```
---
## Test Categories
### 1. Progressive On-Demand Loading Tests ⭐ NEW
**Purpose**: Verify wildcards are loaded progressively as accessed.
**Test Files**:
- `test_progressive_ondemand.sh` (Shell, ~2 min)
- `test_progressive_loading.py` (Python unit test)
#### What's Tested
**Early Termination Size Calculation**:
```python
# Problem: 10GB scan takes 10-30 minutes
# Solution: Stop at cache limit
calculate_directory_size(path, limit=50MB) # < 1 second
```
**YAML Pre-loading + TXT On-Demand**:
```python
# Phase 1 (Startup): Pre-load ALL YAML files
# Reason: Keys are inside file content, not file path
load_yaml_files_only() # colors.yaml → colors, colors/warm, colors/cold
# Phase 2 (Runtime): Load TXT files on-demand
# File path = key (e.g., "flower.txt" → "__flower__")
# No metadata scan for TXT files
```
**Progressive Loading**:
```
Initial: /list/loaded → YAML keys only (e.g., colors, colors/warm, colors/cold)
After __flower__: /list/loaded → +1 TXT wildcard
After __dragon__: /list/loaded → +2-3 (TXT transitive)
```
**⚠️ YAML Limitation**:
YAML wildcards are excluded from on-demand mode because wildcard keys exist
inside the file content. To discover `__colors/warm__`, we must parse `colors.yaml`.
Solution: Convert large YAML collections to TXT file structure for true on-demand.
#### New API Endpoint
**`GET /impact/wildcards/list/loaded`**:
```json
{
"data": ["__colors__", "__colors/warm__", "__colors/cold__", "__samples/flower__"],
"on_demand_mode": true,
"total_available": 0
}
```
Note: `total_available` is 0 in on-demand mode (TXT files not pre-scanned)
**Progressive Example**:
```bash
# Initial state (YAML pre-loaded)
curl /impact/wildcards/list/loaded
→ {"data": ["__colors__", "__colors/warm__", "__colors/cold__"], "total_available": 0}
# Access first wildcard
curl -X POST /impact/wildcards -d '{"text": "__flower__", "seed": 42}'
# Check again (TXT wildcard added)
curl /impact/wildcards/list/loaded
→ {"data": ["__colors__", "__colors/warm__", "__colors/cold__", "__samples/flower__"], "total_available": 0}
```
#### Performance Improvements
**Large Dataset (10GB, 100K files)**:
| Metric | Before | After |
|--------|--------|-------|
| **Startup** | 20-60 min | **< 1 min** |
| **Memory** | 5-10 GB | **< 100MB** |
| **Size calc** | 10-30 min | **< 1 sec** |
#### Run Test
```bash
bash test_progressive_ondemand.sh
```
**Expected Output**:
```
Step 1: Initial state
Loaded wildcards: 0
Step 2: Access __samples/flower__
Loaded wildcards: 1
✓ PASS: Wildcard count increased
Step 3: Access __dragon__
Loaded wildcards: 3
✓ PASS: Wildcard count increased progressively
🎉 ALL TESTS PASSED
```
---
### 2. Lazy Loading Tests
**Purpose**: Verify on-demand loading produces identical results to full cache mode.
**Test Files**:
- `test_lazy_load_api.sh` (Shell, ~3 min)
- `test_wildcard_lazy_loading.py` (Python unit test)
- `test_lazy_load_verification.py` (Python verification)
#### What's Tested
**LazyWildcardLoader Class**:
- Loads data only on first access
- Acts as list-like proxy
- Thread-safe with locking
**Mode Detection**:
- Automatic based on total size vs cache limit
- Full cache: < 50MB (default)
- On-demand: ≥ 50MB
**Consistency**:
- Full cache results == On-demand results
- Same seeds produce same outputs
- All wildcard features work identically
#### Test Scenarios
**test_lazy_load_api.sh** runs both modes and compares:
1. **Wildcard list** (before access)
2. **Simple wildcard**: `__samples/flower__`
3. **Depth 3 transitive**: `__adnd__ creature`
4. **YAML wildcard**: `__colors__`
5. **Wildcard list** (after access)
**All results must match exactly**.
#### Run Test
```bash
bash test_lazy_load_api.sh
```
**Expected Output**:
```
Testing: full_cache (limit: 100MB, port: 8190)
✓ Server started
Test 1: Get wildcard list
Total wildcards: 1000
Testing: on_demand (limit: 1MB, port: 8191)
✓ Server started
Test 1: Get wildcard list
Total wildcards: 1000
COMPARISON RESULTS
Test: Simple Wildcard
✓ Results MATCH
🎉 ALL TESTS PASSED
On-demand loading produces IDENTICAL results!
```
---
### 3. Sequential/Transitive Loading Tests
**Purpose**: Verify transitive wildcards expand correctly across multiple stages.
**Test Files**:
- `test_sequential_loading.sh` (Shell, ~5 min)
- `find_transitive_wildcards.sh` (Utility)
#### What's Tested
**Transitive Expansion**:
```
Depth 1: __samples/flower__ → rose
Depth 2: __dragon__ → __dragon/warrior__ → content
Depth 3: __adnd__ → __dragon__ → __dragon_spirit__ → content
```
**Maximum Depth**: 3 levels verified (system supports up to 100)
#### Test Categories
**17 tests across 5 categories**:
1. **Depth Verification** (4 tests)
- Depth 1: Direct wildcard
- Depth 2: One level transitive
- Depth 3: Two levels + suffix
- Depth 3: Maximum chain
2. **Mixed Transitive** (3 tests)
- Dynamic selection of transitive
- Multiple transitive in one prompt
- Nested transitive in dynamic
3. **Complex Scenarios** (3 tests)
- Weighted selection with transitive
- Multi-select with transitive
- Quantified transitive
4. **Edge Cases** (4 tests)
- Compound grammar
- Multiple wildcards, different depths
- YAML wildcards (no transitive)
- Transitive + YAML combination
5. **On-Demand Mode** (3 tests)
- Depth 3 in on-demand
- Complex scenario in on-demand
- Multiple transitive in on-demand
#### Example: Depth 3 Chain
**Files**:
```
adnd.txt:
__dragon__
dragon.txt:
__dragon_spirit__
dragon_spirit.txt:
Shrewd Hatchling
Ancient Dragon
```
**Usage**:
```
__adnd__ creature
→ __dragon__ creature
→ __dragon_spirit__ creature
→ "Shrewd Hatchling creature"
```
#### Run Test
```bash
bash test_sequential_loading.sh
```
**Expected Output**:
```
=== Test 01: Depth 1 - Direct wildcard ===
Raw prompt: __samples/flower__
✓ All wildcards fully expanded
Final Output: rose
Status: ✅ SUCCESS
=== Test 04: Depth 3 - Maximum transitive chain ===
Raw prompt: __adnd__ creature
✓ All wildcards fully expanded
Final Output: Shrewd Hatchling creature
Status: ✅ SUCCESS
```
---
### 4. Versatile Prompts Tests
**Purpose**: Test all wildcard features and syntax variations.
**Test Files**:
- `test_versatile_prompts.sh` (Shell, ~2 min)
- `test_wildcard_features.sh` (Shell)
- `test_wildcard_consistency.sh` (Shell)
#### What's Tested
**30 prompts across 10 categories**:
1. **Simple Wildcards** (3 tests)
- Basic substitution
- Case insensitive (uppercase)
- Case insensitive (mixed)
2. **Dynamic Prompts** (3 tests)
- Simple: `{red|green|blue} apple`
- Nested: `{a|{d|e|f}|c}`
- Complex nested: `{blue apple|red {cherry|berry}}`
3. **Selection Weights** (2 tests)
- Weighted: `{5::red|4::green|7::blue} car`
- Multiple weighted: `{10::beautiful|5::stunning} {3::sunset|2::sunrise}`
4. **Compound Grammar** (3 tests)
- Wildcard + dynamic: `{pencil|apple|__flower__}`
- Complex compound: `1{girl|boy} {sitting|standing} with {__object__|item}`
- Nested compound: `{big|small} {red {apple|cherry}|blue __flower__}`
5. **Multi-Select** (4 tests)
- Fixed count: `{2$$, $$opt1|opt2|opt3|opt4}`
- Range: `{2-4$$, $$opt1|opt2|opt3|opt4|opt5}`
- With separator: `{3$$; $$a|b|c|d|e}`
- Short form: `{-3$$, $$opt1|opt2|opt3|opt4}`
6. **Quantifiers** (2 tests)
- Basic: `3#__wildcard__`
- With multi-select: `{2$$, $$5#__colors__}`
7. **Wildcard Fallback** (2 tests)
- Auto-expand: `__flower__` → `__*/flower__`
- Wildcard patterns: `__samples/*__`
8. **YAML Wildcards** (3 tests)
- Simple YAML: `__colors__`
- Nested YAML: `__colors/warm__`
- Multiple YAML: `__colors__ and __animals__`
9. **Transitive Wildcards** (4 tests)
- Depth 2: `__dragon__`
- Depth 3: `__adnd__`
- Mixed depth: `__flower__ and __dragon__`
- Dynamic transitive: `{__dragon__|__adnd__}`
10. **Real-World Scenarios** (4 tests)
- Portrait prompt
- Landscape prompt
- Fantasy prompt
- Abstract art prompt
#### Example Tests
**Test 04: Simple Dynamic Prompt**:
```
Raw: {red|green|blue} apple
Seed: 100
Result: "red apple" (deterministic)
```
**Test 09: Wildcard + Dynamic**:
```
Raw: 1girl holding {blue pencil|red apple|colorful __samples/flower__}
Seed: 100
Result: "1girl holding colorful chrysanthemum"
```
**Test 18: Multi-Select Range**:
```
Raw: {2-4$$, $$happy|sad|angry|excited|calm}
Seed: 100
Result: "happy, sad, angry" (2-4 emotions selected)
```
#### Run Test
```bash
bash test_versatile_prompts.sh
```
**Expected Output**:
```
========================================
Test 01: Basic Wildcard
========================================
Raw: __samples/flower__
Result: chrysanthemum
Status: ✅ PASS
========================================
Test 04: Simple Dynamic Prompt
========================================
Raw: {red|green|blue} apple
Result: red apple
Status: ✅ PASS
Total: 30 tests
Passed: 30
Failed: 0
```
---
## Test Execution
### Prerequisites
**Required**:
- ComfyUI installed
- Impact Pack installed
- Python 3.8+
- Bash shell
- curl (for API tests)
**Optional**:
- jq (for JSON parsing)
- git (for version control)
### Environment Setup
**1. Configure Impact Pack**:
```bash
cd /path/to/ComfyUI/custom_nodes/comfyui-impact-pack
# Create or edit config
cat > impact-pack.ini << EOF
[default]
dependency_version = 24
wildcard_cache_limit_mb = 50
custom_wildcards = $(pwd)/custom_wildcards
disable_gpu_opencv = True
EOF
```
**2. Prepare Wildcards**:
```bash
# Check wildcard files exist
ls wildcards/*.txt wildcards/*.yaml
ls custom_wildcards/*.txt
```
### Running Tests
#### Unit Tests (Python)
**Standalone** (no server required):
```bash
python3 test_wildcard_lazy_loading.py
python3 test_progressive_loading.py
```
**Note**: Requires ComfyUI environment or will show import errors.
#### Integration Tests (Shell)
**Manual Server Start**:
```bash
# Terminal 1: Start server
cd /path/to/ComfyUI
bash run.sh --listen 127.0.0.1 --port 8188
# Terminal 2: Run tests
cd custom_nodes/comfyui-impact-pack/tests
bash test_versatile_prompts.sh
```
**Automated** (tests start/stop server):
```bash
# Each test manages its own server
bash test_progressive_ondemand.sh # Port 8195
bash test_lazy_load_api.sh # Ports 8190-8191
bash test_sequential_loading.sh # Port 8193
```
### Test Timing
| Test | Duration | Server | Ports |
|------|----------|--------|-------|
| `test_progressive_ondemand.sh` | ~2 min | Auto | 8195 |
| `test_lazy_load_api.sh` | ~3 min | Auto | 8190-8191 |
| `test_sequential_loading.sh` | ~5 min | Auto | 8193 |
| `test_versatile_prompts.sh` | ~2 min | Manual | 8188 |
| `test_wildcard_consistency.sh` | ~1 min | Manual | 8188 |
| Python unit tests | < 5 sec | No | N/A |
### Logs
**Server Logs**:
```bash
/tmp/progressive_test.log
/tmp/comfyui_full_cache.log
/tmp/comfyui_on_demand.log
/tmp/sequential_test.log
```
**Check Logs**:
```bash
# View recent wildcard logs
tail -50 /tmp/progressive_test.log | grep -i wildcard
# Find errors
grep -i "error\|fail" /tmp/*.log
# Check mode activation
grep -i "mode" /tmp/progressive_test.log
```
---
## Expected Results
### Success Criteria
#### Progressive Loading
- ✅ `/list/loaded` starts at 0 (or low count)
- ✅ `/list/loaded` increases after each unique wildcard
- ✅ `/list/loaded` unchanged on cache hits
- ✅ Transitive wildcards load multiple entries
- ✅ Final results identical to full cache mode
#### Lazy Loading
- ✅ Full cache results == On-demand results (all tests)
- ✅ Mode detection correct (based on size vs limit)
- ✅ LazyWildcardLoader loads only on access
- ✅ All API endpoints return consistent data
#### Sequential Loading
- ✅ Depth 1-3 expand correctly
- ✅ Complex scenarios work (weighted, multi-select, etc.)
- ✅ On-demand mode matches full cache
- ✅ No infinite loops (max 100 iterations)
#### Versatile Prompts
- ✅ All 30 test prompts process successfully
- ✅ Deterministic (same seed → same result)
- ✅ No syntax errors
- ✅ Proper probability distribution
### Sample Output
**Progressive Loading Success**:
```
========================================
Progressive Loading Verification
========================================
Step 1: Initial state
On-demand mode: True
Total available: 1000
Loaded wildcards: 0
Step 2: Access __samples/flower__
Result: rose
Loaded wildcards: 1
✓ PASS
Step 3: Access __dragon__
Result: ancient dragon
Loaded wildcards: 3
✓ PASS
🎉 ALL TESTS PASSED
Progressive on-demand loading verified!
```
**Lazy Loading Success**:
```
========================================
COMPARISON RESULTS
========================================
Test: Wildcard List (before)
✓ Results MATCH
Test: Simple Wildcard
✓ Results MATCH
Test: Depth 3 Transitive
✓ Results MATCH
🎉 ALL TESTS PASSED
On-demand produces IDENTICAL results!
```
---
## Troubleshooting
### Common Issues
#### 1. Server Fails to Start
**Symptoms**:
```
✗ Server failed to start
curl: (7) Failed to connect
```
**Solutions**:
```bash
# Check if port in use
lsof -i :8188
netstat -tlnp | grep 8188
# Kill existing processes
pkill -f "python.*main.py"
# Increase startup wait time
# In test script: sleep 15 → sleep 30
```
#### 2. Module Not Found (Python)
**Symptoms**:
```
ModuleNotFoundError: No module named 'modules'
```
**Solutions**:
```bash
# Option 1: Run from ComfyUI directory
cd /path/to/ComfyUI
python3 custom_nodes/comfyui-impact-pack/tests/test_progressive_loading.py
# Option 2: Add to PYTHONPATH
export PYTHONPATH=/path/to/ComfyUI/custom_nodes/comfyui-impact-pack:$PYTHONPATH
python3 test_progressive_loading.py
```
#### 3. On-Demand Mode Not Activating
**Symptoms**:
```
Using full cache mode.
```
**Check**:
```bash
# View total size
grep "Wildcard total size" /tmp/progressive_test.log
# Check cache limit
grep "cache_limit_mb" impact-pack.ini
```
**Solutions**:
```bash
# Force on-demand mode
cat > impact-pack.ini << EOF
[default]
wildcard_cache_limit_mb = 0.5
EOF
```
#### 4. Tests Timeout
**Symptoms**:
```
Waiting for server startup...
✗ Server failed to start
```
**Solutions**:
```bash
# Check system resources
free -h
df -h
# View server logs
tail -100 /tmp/progressive_test.log
# Manually test server
cd /path/to/ComfyUI
bash run.sh --port 8195
# Increase timeout in test
# sleep 15 → sleep 60
```
#### 5. Results Don't Match
**Symptoms**:
```
✗ Results DIFFER
```
**Debug**:
```bash
# Compare results
diff /tmp/result_full_cache_simple.json /tmp/result_on_demand_simple.json
# Check seeds are same
grep "seed" /tmp/result_*.json
# Verify same wildcard files used
ls -la wildcards/samples/flower.txt
```
**File Bug Report**:
- Wildcard text
- Seed value
- Full cache result
- On-demand result
- Server logs
#### 6. Slow Performance
**Symptoms**:
- Tests take much longer than expected
- Server startup > 2 minutes
**Check**:
```bash
# Wildcard size
du -sh wildcards/
# Disk I/O
iostat -x 1 5
# System resources
top
```
**Solutions**:
- Use SSD (not HDD)
- Reduce wildcard size
- Increase cache limit (use full cache mode)
- Close other applications
---
## Performance Benchmarks
### Expected Performance
**Small Dataset (< 50MB)**:
```
Mode: Full cache
Startup: < 10 seconds
Memory: ~50MB
First access: Instant
```
**Medium Dataset (50MB - 1GB)**:
```
Mode: On-demand
Startup: < 30 seconds
Memory: < 200MB initial
First access: 10-50ms per wildcard
```
**Large Dataset (10GB+)**:
```
Mode: On-demand
Startup: < 1 minute
Memory: < 100MB initial
First access: 10-50ms per wildcard
Memory growth: Progressive
```
### Optimization Tips
**For Faster Tests**:
1. Use smaller wildcard dataset
2. Run specific tests (not all)
3. Use manual server (keep running)
4. Skip sleep times (if server already running)
**For Large Datasets**:
1. Verify on-demand mode activates
2. Monitor `/list/loaded` to track memory
3. Use SSD for file storage
4. Organize wildcards into subdirectories
---
## Contributing
### Adding New Tests
**1. Create Test File**:
```bash
touch tests/test_new_feature.sh
chmod +x tests/test_new_feature.sh
```
**2. Test Template**:
```bash
#!/bin/bash
# Test: New Feature
# Purpose: Verify new feature works correctly
set -e
PORT=8XXX
IMPACT_DIR="/path/to/comfyui-impact-pack"
# Setup config
cat > impact-pack.ini << EOF
[default]
wildcard_cache_limit_mb = 50
EOF
# Start server
cd /path/to/ComfyUI
bash run.sh --port $PORT > /tmp/test_new.log 2>&1 &
sleep 15
# Test
RESULT=$(curl -s http://127.0.0.1:$PORT/impact/wildcards/list)
# Validate
if [ "$RESULT" = "expected" ]; then
echo "✅ PASS"
exit 0
else
echo "❌ FAIL"
exit 1
fi
```
**3. Update Documentation**:
- Add test description to this README
- Update test count
- Add to appropriate category
### Testing Guidelines
**Test Structure**:
1. Clear purpose statement
2. Setup (config, wildcards)
3. Execution (API calls, processing)
4. Validation (assertions, comparisons)
5. Cleanup (kill servers, restore config)
**Good Practices**:
- Use unique port numbers
- Clean up background processes
- Provide clear success/failure messages
- Log to `/tmp/` for debugging
- Use deterministic seeds
- Test both modes (full cache + on-demand)
---
## Reference
### Test Files Quick Reference
```bash
# Progressive loading
test_progressive_ondemand.sh # Integration test
test_progressive_loading.py # Unit test
# Lazy loading
test_lazy_load_api.sh # Integration test
test_wildcard_lazy_loading.py # Unit test
# Sequential/transitive
test_sequential_loading.sh # Integration test
find_transitive_wildcards.sh # Utility
# Features
test_versatile_prompts.sh # Comprehensive features
test_wildcard_features.sh # Core features
test_wildcard_consistency.sh # Consistency
# Validation
test_wildcard_final.py # Final validation
test_lazy_load_verification.py # Lazy load verification
```
### Documentation
- **System Overview**: `../docs/WILDCARD_SYSTEM_OVERVIEW.md`
- **Testing Guide**: `../docs/WILDCARD_TESTING_GUIDE.md`
### API Endpoints
```
GET /impact/wildcards/list # All available wildcards
GET /impact/wildcards/list/loaded # Actually loaded (progressive)
POST /impact/wildcards # Process wildcard text
GET /impact/wildcards/refresh # Reload all wildcards
```
---
**Last Updated**: 2024-11-17
**Total Tests**: 11 files, 100+ scenarios
**Coverage**: ~95% of wildcard features

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#!/usr/bin/env python3
"""Find deep transitive wildcard references (5+ levels)"""
import re
from pathlib import Path
from collections import defaultdict
# Auto-detect paths
SCRIPT_DIR = Path(__file__).parent
IMPACT_PACK_DIR = SCRIPT_DIR.parent
WILDCARDS_DIR = IMPACT_PACK_DIR / "wildcards"
CUSTOM_WILDCARDS_DIR = IMPACT_PACK_DIR / "custom_wildcards"
# Build wildcard reference graph
wildcard_refs = defaultdict(set) # wildcard -> set of wildcards it references
wildcard_files = {} # wildcard_name -> file_path
def normalize_name(name):
"""Normalize wildcard name"""
return name.lower().replace('/', '_').replace('\\', '_')
def find_wildcard_file(name):
"""Find wildcard file by name"""
# Try different variations
variations = [
name,
name.replace('/', '_'),
name.replace('\\', '_'),
]
for var in variations:
# Check in wildcards/
for ext in ['.txt', '.yaml', '.yml']:
path = WILDCARDS_DIR / f"{var}{ext}"
if path.exists():
return str(path)
# Check in custom_wildcards/
for ext in ['.txt', '.yaml', '.yml']:
path = CUSTOM_WILDCARDS_DIR / f"{var}{ext}"
if path.exists():
return str(path)
return None
def scan_wildcards():
"""Scan all wildcard files and build reference graph"""
print("Scanning wildcard files...")
# Find all wildcard files
for base_dir in [WILDCARDS_DIR, CUSTOM_WILDCARDS_DIR]:
for ext in ['*.txt', '*.yaml', '*.yml']:
for file_path in base_dir.rglob(ext):
# Get wildcard name from file path
rel_path = file_path.relative_to(base_dir)
name = str(rel_path.with_suffix('')).replace('/', '_').replace('\\', '_')
wildcard_files[normalize_name(name)] = str(file_path)
# Find references in file
try:
content = file_path.read_text(encoding='utf-8', errors='ignore')
refs = re.findall(r'__([^_]+(?:/[^_]+)*)__', content)
for ref in refs:
ref_normalized = normalize_name(ref)
if ref_normalized and ref_normalized != '':
wildcard_refs[normalize_name(name)].add(ref_normalized)
except Exception as e:
print(f"Error reading {file_path}: {e}")
print(f"Found {len(wildcard_files)} wildcard files")
print(f"Found {sum(len(refs) for refs in wildcard_refs.values())} references")
print()
def find_max_depth(start_wildcard, visited=None, path=None):
"""Find maximum depth of transitive references starting from a wildcard"""
if visited is None:
visited = set()
if path is None:
path = []
if start_wildcard in visited:
return 0, path # Cycle detected
visited.add(start_wildcard)
path.append(start_wildcard)
refs = wildcard_refs.get(start_wildcard, set())
if not refs:
return 1, path # Leaf node
max_depth = 0
max_path = path.copy()
for ref in refs:
if ref in wildcard_files: # Only follow if target exists
depth, sub_path = find_max_depth(ref, visited.copy(), path.copy())
if depth > max_depth:
max_depth = depth
max_path = sub_path
return max_depth + 1, max_path
def main():
scan_wildcards()
# Find wildcards with references
wildcards_with_refs = [(name, refs) for name, refs in wildcard_refs.items() if refs]
print(f"Analyzing {len(wildcards_with_refs)} wildcards with references...")
print()
# Calculate depth for each wildcard
depths = []
for name, refs in wildcards_with_refs:
depth, path = find_max_depth(name)
if depth >= 2: # At least one level of transitive reference
depths.append((depth, name, path))
# Sort by depth (deepest first)
depths.sort(reverse=True)
print("=" * 80)
print("WILDCARD REFERENCE DEPTH ANALYSIS")
print("=" * 80)
print()
# Show top 20 deepest
print("Top 20 Deepest Transitive References:")
print()
for i, (depth, name, path) in enumerate(depths[:20], 1):
print(f"{i}. Depth {depth}: __{name}__")
print(f" Path: {' → '.join(f'__{p}__' for p in path)}")
if name in wildcard_files:
print(f" File: {wildcard_files[name]}")
print()
# Find 5+ depth wildcards
deep_wildcards = [(depth, name, path) for depth, name, path in depths if depth >= 5]
print()
print("=" * 80)
print(f"WILDCARDS WITH 5+ DEPTH ({len(deep_wildcards)} found)")
print("=" * 80)
print()
if deep_wildcards:
for depth, name, path in deep_wildcards:
print(f"🎯 __{name}__ (Depth: {depth})")
print(f" Chain: {' → '.join(f'__{p}__' for p in path)}")
if name in wildcard_files:
print(f" File: {wildcard_files[name]}")
print()
print()
print("=" * 80)
print("RECOMMENDED TEST CASE")
print("=" * 80)
print()
depth, name, path = deep_wildcards[0]
print(f"Use __{name}__ for testing deep transitive loading")
print(f"Depth: {depth} levels")
print(f"Chain: {' → '.join(f'__{p}__' for p in path)}")
print()
print(f"Test input: \"__{name}__\"")
print(f"Expected: Will resolve through {depth} levels to actual content")
else:
print("No wildcards with 5+ depth found.")
print()
if depths:
depth, name, path = depths[0]
print(f"Maximum depth found: {depth}")
print(f"Wildcard: __{name}__")
print(f"Chain: {' → '.join(f'__{p}__' for p in path)}")
if __name__ == "__main__":
main()

113
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#!/bin/bash
# Find transitive wildcard references in the wildcard directories
# Auto-detect paths
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMPACT_PACK_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
WILDCARDS_DIR="$IMPACT_PACK_DIR/wildcards"
CUSTOM_WILDCARDS_DIR="$IMPACT_PACK_DIR/custom_wildcards"
echo "=========================================="
echo "Transitive Wildcard Reference Scanner"
echo "=========================================="
echo ""
echo "Scanning for wildcard references (pattern: __*__)..."
echo ""
# Function to find references in a file
find_references() {
local file=$1
local relative_path=${file#$IMPACT_PACK_DIR/}
# Find all __wildcard__ patterns in the file
local refs=$(grep -o '__[^_]*__' "$file" 2>/dev/null | sort -u)
if [ -n "$refs" ]; then
echo "📄 $relative_path"
echo " References:"
echo "$refs" | while read -r ref; do
# Remove __ from both ends
local clean_ref=${ref#__}
clean_ref=${clean_ref%__}
# Check if referenced file exists
local found=false
# Check in wildcards/
if [ -f "$WILDCARDS_DIR/$clean_ref.txt" ]; then
echo " → $ref (wildcards/$clean_ref.txt) ✓"
found=true
elif [ -f "$WILDCARDS_DIR/$clean_ref.yaml" ]; then
echo " → $ref (wildcards/$clean_ref.yaml) ✓"
found=true
elif [ -f "$WILDCARDS_DIR/$clean_ref.yml" ]; then
echo " → $ref (wildcards/$clean_ref.yml) ✓"
found=true
fi
# Check in custom_wildcards/
if [ -f "$CUSTOM_WILDCARDS_DIR/$clean_ref.txt" ]; then
echo " → $ref (custom_wildcards/$clean_ref.txt) ✓"
found=true
elif [ -f "$CUSTOM_WILDCARDS_DIR/$clean_ref.yaml" ]; then
echo " → $ref (custom_wildcards/$clean_ref.yaml) ✓"
found=true
elif [ -f "$CUSTOM_WILDCARDS_DIR/$clean_ref.yml" ]; then
echo " → $ref (custom_wildcards/$clean_ref.yml) ✓"
found=true
fi
if [ "$found" = false ]; then
echo " → $ref ❌ (not found)"
fi
done
echo ""
fi
}
# Scan TXT files
echo "=== TXT Files with References ==="
echo ""
find "$WILDCARDS_DIR" "$CUSTOM_WILDCARDS_DIR" -name "*.txt" 2>/dev/null | while read -r file; do
find_references "$file"
done
# Scan YAML files
echo ""
echo "=== YAML Files with References ==="
echo ""
find "$WILDCARDS_DIR" "$CUSTOM_WILDCARDS_DIR" -name "*.yaml" -o -name "*.yml" 2>/dev/null | while read -r file; do
find_references "$file"
done
echo ""
echo "=========================================="
echo "Recommended Test Cases"
echo "=========================================="
echo ""
echo "1. Simple TXT wildcard:"
echo " Input: __samples/flower__"
echo " Type: Direct reference (no transitive)"
echo ""
# Find a good transitive TXT example
echo "2. TXT → TXT transitive:"
find "$CUSTOM_WILDCARDS_DIR" -name "*.txt" -exec grep -l "__.*__" {} \; 2>/dev/null | head -1 | while read -r file; do
local basename=$(basename "$file" .txt)
local first_ref=$(grep -o '__[^_]*__' "$file" 2>/dev/null | head -1)
echo " Input: __${basename}__"
echo " Resolves to: $first_ref (and others)"
echo " File: ${file#$IMPACT_PACK_DIR/}"
done
echo ""
echo "3. YAML transitive:"
echo " Input: __colors__"
echo " Resolves to: __cold__ or __warm__ → blue|red|orange|yellow"
echo " File: custom_wildcards/test.yaml"
echo ""
echo "=========================================="
echo "Scan Complete"
echo "=========================================="

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#!/bin/bash
# Quick test for wildcard lazy loading
echo "=========================================="
echo "Wildcard Lazy Load Quick Test"
echo "=========================================="
echo ""
# Test 1: Get wildcard list (before accessing any wildcards)
echo "=== Test 1: Wildcard List (BEFORE access) ==="
curl -s http://127.0.0.1:8188/impact/wildcards/list > /tmp/wc_list_before.json
COUNT_BEFORE=$(cat /tmp/wc_list_before.json | python3 -c "import sys, json; print(len(json.load(sys.stdin).get('data', [])))")
echo "Total wildcards: $COUNT_BEFORE"
echo ""
# Test 2: Simple wildcard
echo "=== Test 2: Simple Wildcard ==="
curl -s -X POST http://127.0.0.1:8188/impact/wildcards \
-H "Content-Type: application/json" \
-d '{"text": "__samples/flower__", "seed": 42}' > /tmp/wc_simple.json
RESULT2=$(cat /tmp/wc_simple.json | python3 -c "import sys, json; print(json.load(sys.stdin).get('text', 'ERROR'))")
echo "Input: __samples/flower__"
echo "Output: $RESULT2"
echo ""
# Test 3: Depth 3 transitive
echo "=== Test 3: Depth 3 Transitive (TXT→TXT→TXT) ==="
curl -s -X POST http://127.0.0.1:8188/impact/wildcards \
-H "Content-Type: application/json" \
-d '{"text": "__adnd__ creature", "seed": 222}' > /tmp/wc_depth3.json
RESULT3=$(cat /tmp/wc_depth3.json | python3 -c "import sys, json; print(json.load(sys.stdin).get('text', 'ERROR'))")
echo "Input: __adnd__ creature"
echo "Output: $RESULT3"
echo "Chain: adnd → (dragon/beast/...) → (dragon_spirit/...)"
echo ""
# Test 4: YAML transitive
echo "=== Test 4: YAML Transitive ==="
curl -s -X POST http://127.0.0.1:8188/impact/wildcards \
-H "Content-Type: application/json" \
-d '{"text": "__colors__", "seed": 333}' > /tmp/wc_yaml.json
RESULT4=$(cat /tmp/wc_yaml.json | python3 -c "import sys, json; print(json.load(sys.stdin).get('text', 'ERROR'))")
echo "Input: __colors__"
echo "Output: $RESULT4"
echo "Chain: colors → (cold|warm) → (blue|red|orange|yellow)"
echo ""
# Test 5: Get wildcard list (AFTER accessing wildcards)
echo "=== Test 5: Wildcard List (AFTER access) ==="
curl -s http://127.0.0.1:8188/impact/wildcards/list > /tmp/wc_list_after.json
COUNT_AFTER=$(cat /tmp/wc_list_after.json | python3 -c "import sys, json; print(len(json.load(sys.stdin).get('data', [])))")
echo "Total wildcards: $COUNT_AFTER"
echo ""
# Compare
echo "=========================================="
echo "Results"
echo "=========================================="
echo ""
if [ "$COUNT_BEFORE" -eq "$COUNT_AFTER" ]; then
echo "✅ Wildcard list unchanged: $COUNT_BEFORE = $COUNT_AFTER"
else
echo "❌ Wildcard list changed: $COUNT_BEFORE != $COUNT_AFTER"
fi
if [ "$RESULT2" != "ERROR" ] && [ "$RESULT3" != "ERROR" ] && [ "$RESULT4" != "ERROR" ]; then
echo "✅ All wildcards resolved successfully"
else
echo "❌ Some wildcards failed"
fi
echo ""
echo "Check /tmp/comfyui_ondemand.log for loading mode"
grep -i "wildcard.*mode" /tmp/comfyui_ondemand.log | tail -1

186
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# Test Wildcard Files Documentation
This directory contains test wildcard files created to validate various features and edge cases of the wildcard system.
## Test Categories
### 1. Error Handling Tests
**test_error_cases.txt**
- Purpose: Test handling of non-existent wildcard references
- Contains: References to `__nonexistent_wildcard__` that should be handled gracefully
- Expected: System should not crash, provide meaningful error or leave unexpanded
**test_circular_a.txt + test_circular_b.txt**
- Purpose: Test circular reference detection (A→B→A)
- Contains: Mutual references between two wildcards
- Expected: System should detect cycle and prevent infinite loop (max 100 iterations)
### 2. Encoding Tests
**test_encoding_utf8.txt**
- Purpose: Test UTF-8 multi-language support
- Contains:
- Emoji: 🌸🌺🌼🌻🌷
- Japanese: さくら, はな, 美しい花, 桜の木
- Chinese: 花, 玫瑰, 莲花, 牡丹
- Korean: 꽃, 장미, 벚꽃
- Arabic (RTL): زهرة, وردة
- Mixed: `🌸 beautiful 美しい flower زهرة 꽃`
- Expected: All characters render correctly, no encoding errors
**test_encoding_emoji.txt**
- Purpose: Test emoji handling across categories
- Contains: Nature, animals, food, hearts, and mixed emoji with text
- Expected: Emojis render correctly in results
**test_encoding_special.txt**
- Purpose: Test special Unicode characters
- Contains:
- Mathematical symbols: ∀∂∃∅∆∇∈∉
- Greek letters: α β γ δ ε ζ
- Currency: $ € £ ¥ ₹ ₽ ₩
- Box drawing: ┌─┬─┐
- Diacritics: Café résumé naïve Zürich
- Special punctuation: … — – • · °
- Expected: All symbols preserved correctly
### 3. Edge Case Tests
**test_edge_empty_lines.txt**
- Purpose: Test handling of empty lines and whitespace-only lines
- Contains: Options separated by variable empty lines
- Expected: Empty lines ignored, only non-empty options selected
**test_edge_whitespace.txt**
- Purpose: Test leading/trailing whitespace handling
- Contains: Options with tabs, spaces, mixed whitespace
- Expected: Whitespace handling according to parser rules
**test_edge_long_lines.txt**
- Purpose: Test very long line handling
- Contains:
- Short lines
- Medium lines (~100 chars)
- Very long lines with spaces (>200 chars)
- Ultra-long lines without spaces (continuous text)
- Expected: No truncation or memory issues, proper handling
**test_edge_special_chars.txt**
- Purpose: Test special characters that might cause parsing issues
- Contains:
- Embedded wildcard syntax: `__wildcard__` as literal text
- Dynamic prompt syntax: `{option|option}` as literal text
- Regex special chars: `.`, `*`, `+`, `?`, `|`, `\`, `$`, `^`
- Quote characters: `"`, `'`, `` ` ``
- HTML special chars: `&`, `<`, `>`, `=`
- Expected: Special chars treated as literal text in final output
**test_edge_case_insensitive.txt**
- Purpose: Validate case-insensitive wildcard matching
- Contains: Options in various case patterns
- Expected: `__test_edge_case_insensitive__` and `__TEST_EDGE_CASE_INSENSITIVE__` return same results
**test_comments.txt**
- Purpose: Test comment handling with `#` prefix
- Contains: Lines starting with `#` mixed with valid options
- Expected: Comment lines ignored, only non-comment lines selected
### 4. Deep Nesting Tests (7 levels)
**test_nesting_level1.txt → test_nesting_level7.txt**
- Purpose: Test transitive wildcard expansion up to 7 levels
- Structure:
- Level 1 → references Level 2
- Level 2 → references Level 3
- ...
- Level 7 → final options (no further references)
- Usage: Access `__test_nesting_level1__` to trigger 7-level expansion
- Expected: All levels expand correctly, result from level 7 appears
### 5. Syntax Feature Tests
**test_quantifier.txt**
- Purpose: Test quantifier syntax `N#__wildcard__`
- Contains: List of color options
- Usage: `3#__test_quantifier__` should expand to 3 repeated wildcards
- Expected: Correct repetition and expansion
**test_pattern_match.txt**
- Purpose: Test pattern matching `__*/name__`
- Contains: Options with identifiable pattern
- Usage: `__*/test_pattern_match__` should match this file
- Expected: Depth-agnostic matching works correctly
## Test Usage Examples
### Basic Test
```bash
curl -X POST http://127.0.0.1:8188/impact/wildcards \
-H "Content-Type: application/json" \
-d '{"text": "__test_encoding_emoji__", "seed": 42}'
```
### Nesting Test
```bash
curl -X POST http://127.0.0.1:8188/impact/wildcards \
-H "Content-Type: application/json" \
-d '{"text": "__test_nesting_level1__", "seed": 42}'
```
### Error Handling Test
```bash
curl -X POST http://127.0.0.1:8188/impact/wildcards \
-H "Content-Type: application/json" \
-d '{"text": "__test_error_cases__", "seed": 42}'
```
### Circular Reference Test
```bash
curl -X POST http://127.0.0.1:8188/impact/wildcards \
-H "Content-Type: application/json" \
-d '{"text": "__test_circular_a__", "seed": 42}'
```
### Quantifier Test
```bash
curl -X POST http://127.0.0.1:8188/impact/wildcards \
-H "Content-Type: application/json" \
-d '{"text": "3#__test_quantifier__", "seed": 42}'
```
### Pattern Matching Test
```bash
curl -X POST http://127.0.0.1:8188/impact/wildcards \
-H "Content-Type: application/json" \
-d '{"text": "__*/test_pattern_match__", "seed": 42}'
```
## Test Coverage
These test files address the following critical gaps identified in the test coverage analysis:
1. ✅ **Error Handling** - Missing wildcard files, circular references
2. ✅ **UTF-8 Encoding** - Multi-language support (emoji, CJK, RTL)
3. ✅ **Edge Cases** - Empty lines, whitespace, long lines, special chars
4. ✅ **Deep Nesting** - 7-level transitive expansion
5. ✅ **Comment Handling** - Lines starting with `#`
6. ✅ **Case Insensitivity** - Case-insensitive wildcard matching
7. ✅ **Pattern Matching** - `__*/name__` syntax
8. ✅ **Quantifiers** - `N#__wildcard__` syntax
## Expected Test Results
All tests should:
- Not crash the system
- Return valid results or graceful error messages
- Preserve character encoding correctly
- Handle edge cases without data corruption
- Respect the 100-iteration limit for circular references
- Demonstrate deterministic behavior with same seed
---
**Created**: 2025-11-18
**Purpose**: Test coverage validation for wildcard system
**Total Files**: 21 test wildcard files

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custom_nodes/ComfyUI-Impact-Pack/tests/wildcards/test_lazy_load_api.sh Executable file
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#!/bin/bash
# Verify wildcard lazy loading through ComfyUI API
set -e
# Auto-detect paths
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMPACT_PACK_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
COMFYUI_DIR="$(cd "$IMPACT_PACK_DIR/../.." && pwd)"
CONFIG_FILE="$IMPACT_PACK_DIR/impact-pack.ini"
BACKUP_CONFIG="$IMPACT_PACK_DIR/impact-pack.ini.backup"
GREEN='\033[0;32m'
RED='\033[0;31m'
BLUE='\033[0;34m'
YELLOW='\033[1;33m'
NC='\033[0m'
echo "=========================================="
echo "Wildcard Lazy Load Verification Test"
echo "=========================================="
echo ""
echo "This test verifies that on-demand loading produces"
echo "identical results to full cache mode."
echo ""
# Backup original config
if [ -f "$CONFIG_FILE" ]; then
cp "$CONFIG_FILE" "$BACKUP_CONFIG"
echo "✓ Backed up original config"
fi
# Cleanup function
cleanup() {
echo ""
echo "Cleaning up..."
pkill -f "python.*main.py" 2>/dev/null || true
sleep 2
}
# Test with specific configuration
test_mode() {
local MODE=$1
local CACHE_LIMIT=$2
local PORT=$3
echo ""
echo "${BLUE}=========================================${NC}"
echo "${BLUE}Testing: $MODE (limit: ${CACHE_LIMIT}MB, port: $PORT)${NC}"
echo "${BLUE}=========================================${NC}"
# Update config
cat > "$CONFIG_FILE" << EOF
[default]
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
custom_wildcards = $IMPACT_PACK_DIR/custom_wildcards
disable_gpu_opencv = True
wildcard_cache_limit_mb = $CACHE_LIMIT
EOF
# Start server
cleanup
cd "$COMFYUI_DIR"
bash run.sh --listen 127.0.0.1 --port $PORT > /tmp/comfyui_${MODE}.log 2>&1 &
COMFYUI_PID=$!
echo "Waiting for server startup..."
sleep 15
# Check server
if ! curl -s http://127.0.0.1:$PORT/ > /dev/null; then
echo "${RED}✗ Server failed to start${NC}"
cat /tmp/comfyui_${MODE}.log | grep -i "wildcard\|error" | tail -20
return 1
fi
# Get loading mode from log
MODE_LOG=$(grep -i "wildcard.*mode" /tmp/comfyui_${MODE}.log | tail -1)
echo "${YELLOW}$MODE_LOG${NC}"
echo ""
# Test 1: Get wildcard list (BEFORE any access in on-demand mode)
echo "📋 Test 1: Get wildcard list"
LIST_RESULT=$(curl -s http://127.0.0.1:$PORT/impact/wildcards/list)
LIST_COUNT=$(echo "$LIST_RESULT" | python3 -c "import sys, json; print(len(json.load(sys.stdin)['data']))")
echo " Total wildcards: $LIST_COUNT"
echo " Sample: $(echo "$LIST_RESULT" | python3 -c "import sys, json; print(', '.join(json.load(sys.stdin)['data'][:10]))")"
echo "$LIST_RESULT" > /tmp/result_${MODE}_list.json
echo ""
# Test 2: Simple wildcard
echo "📋 Test 2: Simple wildcard"
RESULT1=$(curl -s http://127.0.0.1:$PORT/impact/wildcards \
-X POST \
-H "Content-Type: application/json" \
-d '{"text": "__samples/flower__", "seed": 42}')
TEXT1=$(echo "$RESULT1" | python3 -c "import sys, json; print(json.load(sys.stdin)['text'])")
echo " Input: __samples/flower__"
echo " Output: $TEXT1"
echo "$RESULT1" > /tmp/result_${MODE}_simple.json
echo ""
# Test 3: Depth 3 transitive (adnd → dragon → dragon_spirit)
echo "📋 Test 3: Depth 3 transitive (TXT → TXT → TXT)"
RESULT2=$(curl -s http://127.0.0.1:$PORT/impact/wildcards \
-X POST \
-H "Content-Type: application/json" \
-d '{"text": "__adnd__ creature", "seed": 222}')
TEXT2=$(echo "$RESULT2" | python3 -c "import sys, json; print(json.load(sys.stdin)['text'])")
echo " Input: __adnd__ creature (depth 3: adnd → dragon → dragon_spirit)"
echo " Output: $TEXT2"
echo "$RESULT2" > /tmp/result_${MODE}_depth3.json
echo ""
# Test 4: YAML transitive (colors → cold/warm → blue/red/orange/yellow)
echo "📋 Test 4: YAML transitive"
RESULT3=$(curl -s http://127.0.0.1:$PORT/impact/wildcards \
-X POST \
-H "Content-Type: application/json" \
-d '{"text": "__colors__", "seed": 333}')
TEXT3=$(echo "$RESULT3" | python3 -c "import sys, json; print(json.load(sys.stdin)['text'])")
echo " Input: __colors__ (YAML: colors → cold|warm → blue|red|orange|yellow)"
echo " Output: $TEXT3"
echo "$RESULT3" > /tmp/result_${MODE}_yaml.json
echo ""
# Test 5: Get wildcard list AGAIN (AFTER access in on-demand mode)
echo "📋 Test 5: Get wildcard list (after access)"
LIST_RESULT2=$(curl -s http://127.0.0.1:$PORT/impact/wildcards/list)
LIST_COUNT2=$(echo "$LIST_RESULT2" | python3 -c "import sys, json; print(len(json.load(sys.stdin)['data']))")
echo " Total wildcards: $LIST_COUNT2"
echo "$LIST_RESULT2" > /tmp/result_${MODE}_list_after.json
echo ""
# Compare before/after list
if [ "$MODE" = "on_demand" ]; then
if [ "$LIST_COUNT" -eq "$LIST_COUNT2" ]; then
echo "${GREEN}✓ Wildcard list unchanged after access (${LIST_COUNT} = ${LIST_COUNT2})${NC}"
else
echo "${RED}✗ Wildcard list changed after access (${LIST_COUNT} != ${LIST_COUNT2})${NC}"
fi
echo ""
fi
cleanup
echo "${GREEN}✓ $MODE tests completed${NC}"
echo ""
}
# Run tests
test_mode "full_cache" 100 8190
test_mode "on_demand" 1 8191
# Compare results
echo ""
echo "=========================================="
echo "COMPARISON RESULTS"
echo "=========================================="
echo ""
compare_test() {
local TEST_NAME=$1
local FILE_SUFFIX=$2
echo "Test: $TEST_NAME"
DIFF=$(diff /tmp/result_full_cache_${FILE_SUFFIX}.json /tmp/result_on_demand_${FILE_SUFFIX}.json || true)
if [ -z "$DIFF" ]; then
echo "${GREEN}✓ Results MATCH${NC}"
else
echo "${RED}✗ Results DIFFER${NC}"
echo "Difference:"
echo "$DIFF" | head -10
fi
echo ""
}
compare_test "Wildcard List (before access)" "list"
compare_test "Simple Wildcard" "simple"
compare_test "Depth 3 Transitive" "depth3"
compare_test "YAML Transitive" "yaml"
compare_test "Wildcard List (after access)" "list_after"
# Summary
echo "=========================================="
echo "SUMMARY"
echo "=========================================="
echo ""
ALL_MATCH=true
for suffix in list simple depth3 yaml list_after; do
if ! diff /tmp/result_full_cache_${suffix}.json /tmp/result_on_demand_${suffix}.json > /dev/null 2>&1; then
ALL_MATCH=false
break
fi
done
if [ "$ALL_MATCH" = true ]; then
echo "${GREEN}🎉 ALL TESTS PASSED${NC}"
echo "${GREEN}On-demand loading produces IDENTICAL results to full cache mode!${NC}"
EXIT_CODE=0
else
echo "${RED}❌ TESTS FAILED${NC}"
echo "${RED}On-demand loading has consistency issues!${NC}"
EXIT_CODE=1
fi
echo ""
# Restore config
if [ -f "$BACKUP_CONFIG" ]; then
mv "$BACKUP_CONFIG" "$CONFIG_FILE"
echo "✓ Restored original config"
fi
cleanup
echo ""
echo "=========================================="
echo "Test Complete"
echo "=========================================="
exit $EXIT_CODE

262
custom_nodes/ComfyUI-Impact-Pack/tests/wildcards/test_lazy_load_verification.py Normal file
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#!/usr/bin/env python3
"""
Verify that wildcard lists are identical before and after on-demand loading.
This test ensures that LazyWildcardLoader maintains consistency:
1. Full cache mode: all data loaded immediately
2. On-demand mode (before access): LazyWildcardLoader proxies
3. On-demand mode (after access): data loaded on demand
All three scenarios should produce identical wildcard lists and values.
"""
import sys
import os
# Add parent directory to path
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from modules.impact import config
from modules.impact.wildcards import wildcard_load, wildcard_dict, is_on_demand_mode, process
def get_wildcard_list():
"""Get list of all wildcard keys"""
return sorted(list(wildcard_dict.keys()))
def get_wildcard_sample_values(wildcards_to_test=None):
"""Get sample values from specific wildcards"""
if wildcards_to_test is None:
wildcards_to_test = [
'samples/flower',
'samples/jewel',
'adnd', # Depth 3 transitive
'all', # Depth 3 transitive
'colors', # YAML transitive
]
values = {}
for key in wildcards_to_test:
if key in wildcard_dict:
data = wildcard_dict[key]
# Convert to list if it's a LazyWildcardLoader
if hasattr(data, 'get_data'):
data = data.get_data()
values[key] = list(data) if data else []
else:
values[key] = None
return values
def test_full_cache_mode():
"""Test with full cache mode (limit = 100 MB)"""
print("=" * 80)
print("TEST 1: Full Cache Mode")
print("=" * 80)
print()
# Set high cache limit to force full cache mode
config.get_config()['wildcard_cache_limit_mb'] = 100
# Reload wildcards
wildcard_load()
# Check mode
mode = is_on_demand_mode()
print(f"Mode: {'On-Demand' if mode else 'Full Cache'}")
assert not mode, "Should be in Full Cache mode"
# Get wildcard list
wc_list = get_wildcard_list()
print(f"Total wildcards: {len(wc_list)}")
print(f"Sample wildcards: {wc_list[:10]}")
print()
# Get sample values
values = get_wildcard_sample_values()
print("Sample values:")
for key, val in values.items():
if val is not None:
print(f" {key}: {len(val)} items - {val[:3] if len(val) >= 3 else val}")
else:
print(f" {key}: NOT FOUND")
print()
return {
'mode': 'full_cache',
'wildcard_list': wc_list,
'values': values,
}
def test_on_demand_mode_before_access():
"""Test with on-demand mode before accessing data"""
print("=" * 80)
print("TEST 2: On-Demand Mode (Before Access)")
print("=" * 80)
print()
# Set low cache limit to force on-demand mode
config.get_config()['wildcard_cache_limit_mb'] = 1
# Reload wildcards
wildcard_load()
# Check mode
mode = is_on_demand_mode()
print(f"Mode: {'On-Demand' if mode else 'Full Cache'}")
assert mode, "Should be in On-Demand mode"
# Get wildcard list (should work even without loading data)
wc_list = get_wildcard_list()
print(f"Total wildcards: {len(wc_list)}")
print(f"Sample wildcards: {wc_list[:10]}")
print()
# Check that wildcards are LazyWildcardLoader instances
lazy_count = sum(1 for k in wc_list if hasattr(wildcard_dict[k], 'get_data'))
print(f"LazyWildcardLoader instances: {lazy_count}/{len(wc_list)}")
print()
return {
'mode': 'on_demand_before',
'wildcard_list': wc_list,
'lazy_count': lazy_count,
}
def test_on_demand_mode_after_access():
"""Test with on-demand mode after accessing data"""
print("=" * 80)
print("TEST 3: On-Demand Mode (After Access)")
print("=" * 80)
print()
# Mode should still be on-demand from previous test
mode = is_on_demand_mode()
print(f"Mode: {'On-Demand' if mode else 'Full Cache'}")
assert mode, "Should still be in On-Demand mode"
# Get sample values (this will trigger lazy loading)
values = get_wildcard_sample_values()
print("Sample values (after access):")
for key, val in values.items():
if val is not None:
print(f" {key}: {len(val)} items - {val[:3] if len(val) >= 3 else val}")
else:
print(f" {key}: NOT FOUND")
print()
# Test deep transitive wildcards
print("Testing deep transitive wildcards:")
test_cases = [
("__adnd__", 42), # Depth 3: adnd → dragon → dragon_spirit
("__all__", 123), # Depth 3: all → giant → giant_soldier
]
for wildcard_text, seed in test_cases:
result = process(wildcard_text, seed)
print(f" {wildcard_text} (seed={seed}): {result}")
print()
return {
'mode': 'on_demand_after',
'wildcard_list': get_wildcard_list(),
'values': values,
}
def compare_results(result1, result2, result3):
"""Compare results from all three tests"""
print("=" * 80)
print("COMPARISON RESULTS")
print("=" * 80)
print()
# Compare wildcard lists
list1 = result1['wildcard_list']
list2 = result2['wildcard_list']
list3 = result3['wildcard_list']
print("1. Wildcard List Comparison")
print(f" Full Cache: {len(list1)} wildcards")
print(f" On-Demand (before): {len(list2)} wildcards")
print(f" On-Demand (after): {len(list3)} wildcards")
if list1 == list2 == list3:
print(" ✅ All lists are IDENTICAL")
else:
print(" ❌ Lists DIFFER")
if list1 != list2:
print(f" Full Cache vs On-Demand (before): {len(set(list1) - set(list2))} differences")
if list1 != list3:
print(f" Full Cache vs On-Demand (after): {len(set(list1) - set(list3))} differences")
if list2 != list3:
print(f" On-Demand (before) vs On-Demand (after): {len(set(list2) - set(list3))} differences")
print()
# Compare sample values
values1 = result1['values']
values3 = result3['values']
print("2. Sample Values Comparison")
all_match = True
for key in values1.keys():
v1 = values1[key]
v3 = values3[key]
if v1 == v3:
status = "✅ MATCH"
else:
status = "❌ DIFFER"
all_match = False
print(f" {key}: {status}")
if v1 != v3:
print(f" Full Cache: {len(v1) if v1 else 0} items")
print(f" On-Demand: {len(v3) if v3 else 0} items")
print()
if all_match:
print("✅ ALL VALUES MATCH - On-demand loading is CONSISTENT")
else:
print("❌ VALUES DIFFER - On-demand loading has ISSUES")
print()
return list1 == list2 == list3 and all_match
def main():
print()
print("=" * 80)
print("WILDCARD LAZY LOAD VERIFICATION TEST")
print("=" * 80)
print()
print("This test verifies that on-demand loading produces identical results")
print("to full cache mode.")
print()
# Run tests
result1 = test_full_cache_mode()
result2 = test_on_demand_mode_before_access()
result3 = test_on_demand_mode_after_access()
# Compare results
success = compare_results(result1, result2, result3)
# Final result
print("=" * 80)
if success:
print("🎉 TEST PASSED - Lazy loading is working correctly!")
else:
print("❌ TEST FAILED - Lazy loading has consistency issues!")
print("=" * 80)
print()
return 0 if success else 1
if __name__ == '__main__':
sys.exit(main())

247
custom_nodes/ComfyUI-Impact-Pack/tests/wildcards/test_progressive_loading.py Executable file
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#!/usr/bin/env python3
"""
Progressive On-Demand Wildcard Loading Unit Tests
Tests that wildcard loading happens progressively as wildcards are accessed.
"""
import sys
import os
import tempfile
# Add parent directory to path
test_dir = os.path.dirname(os.path.abspath(__file__))
impact_pack_dir = os.path.dirname(test_dir)
sys.path.insert(0, impact_pack_dir)
from modules.impact import wildcards
def test_early_termination():
"""Test that calculate_directory_size stops early when limit exceeded"""
print("=" * 60)
print("TEST 1: Early Termination Size Calculation")
print("=" * 60)
# Create temporary directory with test files
with tempfile.TemporaryDirectory() as tmpdir:
# Create files totaling 100 bytes
for i in range(10):
with open(os.path.join(tmpdir, f"test{i}.txt"), 'w') as f:
f.write("x" * 10) # 10 bytes each
# Test without limit (should scan all)
total_size = wildcards.calculate_directory_size(tmpdir)
print(f"✓ Total size without limit: {total_size} bytes")
assert total_size == 100, f"Expected 100 bytes, got {total_size}"
# Test with limit (should stop early)
limited_size = wildcards.calculate_directory_size(tmpdir, limit=50)
print(f"✓ Size with 50 byte limit: {limited_size} bytes")
assert limited_size >= 50, f"Expected >= 50 bytes, got {limited_size}"
assert limited_size <= total_size, "Limited should not exceed total"
print(f"✓ Early termination working (stopped at {limited_size} bytes)")
print("\n✅ Early termination test PASSED\n")
def test_metadata_scan():
"""Test that scan_wildcard_metadata only scans file paths, not data"""
print("=" * 60)
print("TEST 2: Metadata-Only Scan")
print("=" * 60)
# Create temporary wildcard directory
with tempfile.TemporaryDirectory() as tmpdir:
# Create test files
test_file1 = os.path.join(tmpdir, "test1.txt")
test_file2 = os.path.join(tmpdir, "test2.txt")
test_yaml = os.path.join(tmpdir, "test3.yaml")
with open(test_file1, 'w') as f:
f.write("option1a\noption1b\noption1c\n")
with open(test_file2, 'w') as f:
f.write("option2a\noption2b\n")
with open(test_yaml, 'w') as f:
f.write("key1:\n - value1\n - value2\n")
# Clear globals
wildcards.available_wildcards = {}
wildcards.loaded_wildcards = {}
# Scan metadata only
print(f"✓ Scanning directory: {tmpdir}")
discovered = wildcards.scan_wildcard_metadata(tmpdir)
print(f"✓ Discovered {discovered} wildcards")
assert discovered == 3, f"Expected 3 wildcards, got {discovered}"
print(f"✓ Available wildcards: {list(wildcards.available_wildcards.keys())}")
assert len(wildcards.available_wildcards) == 3
# Verify that data is NOT loaded
assert len(wildcards.loaded_wildcards) == 0, "Data should not be loaded yet"
print("✓ No data loaded (metadata only)")
# Verify file paths are stored
for key in wildcards.available_wildcards.keys():
file_path = wildcards.available_wildcards[key]
assert os.path.exists(file_path), f"File path should exist: {file_path}"
print(f" - {key} -> {file_path}")
print("\n✅ Metadata scan test PASSED\n")
def test_progressive_loading():
"""Test that wildcards are loaded progressively on access"""
print("=" * 60)
print("TEST 3: Progressive On-Demand Loading")
print("=" * 60)
# Create temporary wildcard directory
with tempfile.TemporaryDirectory() as tmpdir:
# Create test files
test_file1 = os.path.join(tmpdir, "wildcard1.txt")
test_file2 = os.path.join(tmpdir, "wildcard2.txt")
test_file3 = os.path.join(tmpdir, "wildcard3.txt")
with open(test_file1, 'w') as f:
f.write("option1a\noption1b\n")
with open(test_file2, 'w') as f:
f.write("option2a\noption2b\n")
with open(test_file3, 'w') as f:
f.write("option3a\noption3b\n")
# Clear globals
wildcards.available_wildcards = {}
wildcards.loaded_wildcards = {}
wildcards._on_demand_mode = True
# Scan metadata
discovered = wildcards.scan_wildcard_metadata(tmpdir)
print(f"✓ Discovered {discovered} wildcards")
print(f"✓ Available: {len(wildcards.available_wildcards)}")
print(f"✓ Loaded: {len(wildcards.loaded_wildcards)}")
# Initial state: 3 available, 0 loaded
assert len(wildcards.available_wildcards) == 3
assert len(wildcards.loaded_wildcards) == 0
# Access first wildcard
print("\nAccessing wildcard1...")
data1 = wildcards.get_wildcard_value("wildcard1")
assert data1 is not None, "Should load wildcard1"
assert len(data1) == 2, f"Expected 2 options, got {len(data1)}"
print(f"✓ Loaded wildcard1: {data1}")
print(f"✓ Loaded count: {len(wildcards.loaded_wildcards)}")
assert len(wildcards.loaded_wildcards) == 1, "Should have 1 loaded wildcard"
# Access second wildcard
print("\nAccessing wildcard2...")
data2 = wildcards.get_wildcard_value("wildcard2")
assert data2 is not None, "Should load wildcard2"
print(f"✓ Loaded wildcard2: {data2}")
print(f"✓ Loaded count: {len(wildcards.loaded_wildcards)}")
assert len(wildcards.loaded_wildcards) == 2, "Should have 2 loaded wildcards"
# Re-access first wildcard (should use cache)
print("\nRe-accessing wildcard1 (cached)...")
data1_again = wildcards.get_wildcard_value("wildcard1")
assert data1_again == data1, "Cached data should match"
print("✓ Cache hit, data matches")
print(f"✓ Loaded count: {len(wildcards.loaded_wildcards)}")
assert len(wildcards.loaded_wildcards) == 2, "Count should not increase on cache hit"
# Access third wildcard
print("\nAccessing wildcard3...")
data3 = wildcards.get_wildcard_value("wildcard3")
assert data3 is not None, "Should load wildcard3"
print(f"✓ Loaded wildcard3: {data3}")
print(f"✓ Loaded count: {len(wildcards.loaded_wildcards)}")
assert len(wildcards.loaded_wildcards) == 3, "Should have 3 loaded wildcards"
# Verify all loaded
assert set(wildcards.loaded_wildcards.keys()) == {"wildcard1", "wildcard2", "wildcard3"}
print("✓ All wildcards loaded progressively")
print("\n✅ Progressive loading test PASSED\n")
def test_wildcard_list_functions():
"""Test get_wildcard_list() and get_loaded_wildcard_list()"""
print("=" * 60)
print("TEST 4: Wildcard List Functions")
print("=" * 60)
# Create temporary wildcard directory
with tempfile.TemporaryDirectory() as tmpdir:
# Create test files
for i in range(5):
with open(os.path.join(tmpdir, f"test{i}.txt"), 'w') as f:
f.write(f"option{i}a\noption{i}b\n")
# Clear globals
wildcards.available_wildcards = {}
wildcards.loaded_wildcards = {}
wildcards._on_demand_mode = True
# Scan metadata
wildcards.scan_wildcard_metadata(tmpdir)
# Test get_wildcard_list (should return all available)
all_wildcards = wildcards.get_wildcard_list()
print(f"✓ get_wildcard_list(): {len(all_wildcards)} wildcards")
assert len(all_wildcards) == 5, "Should return all available wildcards"
# Test get_loaded_wildcard_list (should return 0 initially)
loaded_wildcards_list = wildcards.get_loaded_wildcard_list()
print(f"✓ get_loaded_wildcard_list(): {len(loaded_wildcards_list)} wildcards (initial)")
assert len(loaded_wildcards_list) == 0, "Should return no loaded wildcards initially"
# Load some wildcards
wildcards.get_wildcard_value("test0")
wildcards.get_wildcard_value("test1")
# Test get_loaded_wildcard_list (should return 2 now)
loaded_wildcards_list = wildcards.get_loaded_wildcard_list()
print(f"✓ get_loaded_wildcard_list(): {len(loaded_wildcards_list)} wildcards (after loading 2)")
assert len(loaded_wildcards_list) == 2, "Should return 2 loaded wildcards"
# Verify loaded list is subset of available list
assert set(loaded_wildcards_list).issubset(set(all_wildcards)), "Loaded should be subset of available"
print("✓ Loaded list is subset of available list")
print("\n✅ Wildcard list functions test PASSED\n")
def main():
"""Run all tests"""
print("\n" + "=" * 60)
print("PROGRESSIVE ON-DEMAND LOADING TEST SUITE")
print("=" * 60 + "\n")
try:
test_early_termination()
test_metadata_scan()
test_progressive_loading()
test_wildcard_list_functions()
print("=" * 60)
print("✅ ALL TESTS PASSED")
print("=" * 60)
return 0
except Exception as e:
print("\n" + "=" * 60)
print(f"❌ TEST FAILED: {e}")
print("=" * 60)
import traceback
traceback.print_exc()
return 1
if __name__ == "__main__":
sys.exit(main())

270
custom_nodes/ComfyUI-Impact-Pack/tests/wildcards/test_progressive_ondemand.sh Executable file
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#!/bin/bash
# Progressive On-Demand Wildcard Loading Test
# Verifies that wildcards are loaded progressively as they are accessed
set -e
# Auto-detect paths
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMPACT_PACK_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
COMFYUI_DIR="$(cd "$IMPACT_PACK_DIR/../.." && pwd)"
CONFIG_FILE="$IMPACT_PACK_DIR/impact-pack.ini"
BACKUP_CONFIG="$IMPACT_PACK_DIR/impact-pack.ini.backup"
PORT=8195
GREEN='\033[0;32m'
RED='\033[0;31m'
BLUE='\033[0;34m'
YELLOW='\033[1;33m'
CYAN='\033[0;36m'
NC='\033[0m'
echo "=========================================="
echo "Progressive On-Demand Loading Test"
echo "=========================================="
echo ""
echo "This test verifies that /wildcards/list/loaded"
echo "increases progressively as wildcards are accessed."
echo ""
# Backup original config
if [ -f "$CONFIG_FILE" ]; then
cp "$CONFIG_FILE" "$BACKUP_CONFIG"
echo "✓ Backed up original config"
fi
# Cleanup function
cleanup() {
echo ""
echo "Cleaning up..."
pkill -f "python.*main.py.*$PORT" 2>/dev/null || true
sleep 2
}
# Setup on-demand mode (low cache limit)
echo "${BLUE}Setting up on-demand mode configuration${NC}"
cat > "$CONFIG_FILE" << EOF
[default]
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
custom_wildcards = $IMPACT_PACK_DIR/custom_wildcards
disable_gpu_opencv = True
wildcard_cache_limit_mb = 0.5
EOF
echo "✓ Configuration: on-demand mode (0.5MB limit)"
echo ""
# Start server
cleanup
cd "$COMFYUI_DIR"
echo "Starting ComfyUI server on port $PORT..."
bash run.sh --listen 127.0.0.1 --port $PORT > /tmp/progressive_test.log 2>&1 &
COMFYUI_PID=$!
echo "Waiting for server startup..."
sleep 15
# Check server
if ! curl -s http://127.0.0.1:$PORT/ > /dev/null; then
echo "${RED}✗ Server failed to start${NC}"
cat /tmp/progressive_test.log | grep -i "wildcard\|error" | tail -20
exit 1
fi
echo "${GREEN}✓ Server started${NC}"
echo ""
# Check loading mode from log
MODE_LOG=$(grep -i "wildcard.*mode" /tmp/progressive_test.log | tail -1)
echo "${YELLOW}$MODE_LOG${NC}"
echo ""
# Test Progressive Loading
echo "=========================================="
echo "Progressive Loading Verification"
echo "=========================================="
echo ""
# Step 1: Initial state (no wildcards accessed)
echo "${CYAN}Step 1: Initial state (before any wildcard access)${NC}"
RESPONSE=$(curl -s http://127.0.0.1:$PORT/impact/wildcards/list/loaded)
LOADED_COUNT=$(echo "$RESPONSE" | python3 -c "import sys, json; print(len(json.load(sys.stdin)['data']))" 2>/dev/null || echo "0")
ON_DEMAND=$(echo "$RESPONSE" | python3 -c "import sys, json; print(json.load(sys.stdin).get('on_demand_mode', False))" 2>/dev/null || echo "false")
TOTAL_AVAILABLE=$(echo "$RESPONSE" | python3 -c "import sys, json; print(json.load(sys.stdin).get('total_available', 0))" 2>/dev/null || echo "0")
echo " On-demand mode: $ON_DEMAND"
echo " Total available wildcards: $TOTAL_AVAILABLE"
echo " Loaded wildcards: ${YELLOW}$LOADED_COUNT${NC}"
if [ "$ON_DEMAND" != "True" ]; then
echo "${RED}✗ FAIL: On-demand mode not active!${NC}"
exit 1
fi
if [ "$LOADED_COUNT" -ne 0 ]; then
echo "${YELLOW}⚠ WARNING: Expected 0 loaded, got $LOADED_COUNT${NC}"
fi
echo ""
# Step 2: Access first wildcard
echo "${CYAN}Step 2: Access first wildcard (__samples/flower__)${NC}"
RESULT1=$(curl -s http://127.0.0.1:$PORT/impact/wildcards \
-X POST \
-H "Content-Type: application/json" \
-d '{"text": "__samples/flower__", "seed": 42}')
TEXT1=$(echo "$RESULT1" | python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))")
echo " Result: $TEXT1"
RESPONSE=$(curl -s http://127.0.0.1:$PORT/impact/wildcards/list/loaded)
LOADED_COUNT_1=$(echo "$RESPONSE" | python3 -c "import sys, json; print(len(json.load(sys.stdin)['data']))")
echo " Loaded wildcards: ${YELLOW}$LOADED_COUNT_1${NC}"
if [ "$LOADED_COUNT_1" -lt 1 ]; then
echo "${RED}✗ FAIL: Expected at least 1 loaded wildcard${NC}"
exit 1
fi
echo "${GREEN}✓ PASS: Wildcard count increased${NC}"
echo ""
# Step 3: Access second wildcard (different from first)
echo "${CYAN}Step 3: Access second wildcard (__dragon__)${NC}"
RESULT2=$(curl -s http://127.0.0.1:$PORT/impact/wildcards \
-X POST \
-H "Content-Type: application/json" \
-d '{"text": "__dragon__", "seed": 200}')
TEXT2=$(echo "$RESULT2" | python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))")
echo " Result: $TEXT2"
RESPONSE=$(curl -s http://127.0.0.1:$PORT/impact/wildcards/list/loaded)
LOADED_COUNT_2=$(echo "$RESPONSE" | python3 -c "import sys, json; print(len(json.load(sys.stdin)['data']))")
echo " Loaded wildcards: ${YELLOW}$LOADED_COUNT_2${NC}"
if [ "$LOADED_COUNT_2" -le "$LOADED_COUNT_1" ]; then
echo "${RED}✗ FAIL: Expected loaded count to increase (was $LOADED_COUNT_1, now $LOADED_COUNT_2)${NC}"
exit 1
fi
echo "${GREEN}✓ PASS: Wildcard count increased progressively${NC}"
echo ""
# Step 4: Access third wildcard (YAML)
echo "${CYAN}Step 4: Access third wildcard (__colors__)${NC}"
RESULT3=$(curl -s http://127.0.0.1:$PORT/impact/wildcards \
-X POST \
-H "Content-Type: application/json" \
-d '{"text": "__colors__", "seed": 333}')
TEXT3=$(echo "$RESULT3" | python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))")
echo " Result: $TEXT3"
RESPONSE=$(curl -s http://127.0.0.1:$PORT/impact/wildcards/list/loaded)
LOADED_COUNT_3=$(echo "$RESPONSE" | python3 -c "import sys, json; print(len(json.load(sys.stdin)['data']))")
LOADED_LIST=$(echo "$RESPONSE" | python3 -c "import sys, json; print(', '.join(json.load(sys.stdin)['data'][:10]))")
echo " Loaded wildcards: ${YELLOW}$LOADED_COUNT_3${NC}"
echo " Sample loaded: $LOADED_LIST"
if [ "$LOADED_COUNT_3" -le "$LOADED_COUNT_2" ]; then
echo "${RED}✗ FAIL: Expected loaded count to increase (was $LOADED_COUNT_2, now $LOADED_COUNT_3)${NC}"
exit 1
fi
echo "${GREEN}✓ PASS: Wildcard count increased progressively${NC}"
echo ""
# Step 5: Re-access first wildcard (should not increase count)
echo "${CYAN}Step 5: Re-access first wildcard (cached)${NC}"
RESULT4=$(curl -s http://127.0.0.1:$PORT/impact/wildcards \
-X POST \
-H "Content-Type: application/json" \
-d '{"text": "__samples/flower__", "seed": 42}')
RESPONSE=$(curl -s http://127.0.0.1:$PORT/impact/wildcards/list/loaded)
LOADED_COUNT_4=$(echo "$RESPONSE" | python3 -c "import sys, json; print(len(json.load(sys.stdin)['data']))")
echo " Loaded wildcards: ${YELLOW}$LOADED_COUNT_4${NC}"
if [ "$LOADED_COUNT_4" -ne "$LOADED_COUNT_3" ]; then
echo "${YELLOW}⚠ WARNING: Count changed on cache access (was $LOADED_COUNT_3, now $LOADED_COUNT_4)${NC}"
else
echo "${GREEN}✓ PASS: Cached access did not change count${NC}"
fi
echo ""
# Step 6: Deep transitive wildcard (should load multiple wildcards)
echo "${CYAN}Step 6: Deep transitive wildcard (__adnd__)${NC}"
RESULT5=$(curl -s http://127.0.0.1:$PORT/impact/wildcards \
-X POST \
-H "Content-Type: application/json" \
-d '{"text": "__adnd__ creature", "seed": 222}')
TEXT5=$(echo "$RESULT5" | python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))")
echo " Result: $TEXT5"
RESPONSE=$(curl -s http://127.0.0.1:$PORT/impact/wildcards/list/loaded)
LOADED_COUNT_5=$(echo "$RESPONSE" | python3 -c "import sys, json; print(len(json.load(sys.stdin)['data']))")
echo " Loaded wildcards: ${YELLOW}$LOADED_COUNT_5${NC}"
if [ "$LOADED_COUNT_5" -le "$LOADED_COUNT_4" ]; then
echo "${YELLOW}⚠ Transitive wildcards may already be loaded${NC}"
else
echo "${GREEN}✓ PASS: Transitive wildcards loaded progressively${NC}"
fi
echo ""
# Summary
echo "=========================================="
echo "Progressive Loading Summary"
echo "=========================================="
echo ""
echo "Total available wildcards: $TOTAL_AVAILABLE"
echo "Loading progression:"
echo " Initial: $LOADED_COUNT"
echo " After step 2: $LOADED_COUNT_1 (+$(($LOADED_COUNT_1 - $LOADED_COUNT)))"
echo " After step 3: $LOADED_COUNT_2 (+$(($LOADED_COUNT_2 - $LOADED_COUNT_1)))"
echo " After step 4: $LOADED_COUNT_3 (+$(($LOADED_COUNT_3 - $LOADED_COUNT_2)))"
echo " After step 5: $LOADED_COUNT_4 (cache, no change)"
echo " After step 6: $LOADED_COUNT_5 (+$(($LOADED_COUNT_5 - $LOADED_COUNT_4)))"
echo ""
# Validation
ALL_PASSED=true
if [ "$LOADED_COUNT_1" -le "$LOADED_COUNT" ]; then
echo "${RED}✗ FAIL: Step 2 did not increase count${NC}"
ALL_PASSED=false
fi
if [ "$LOADED_COUNT_2" -le "$LOADED_COUNT_1" ]; then
echo "${RED}✗ FAIL: Step 3 did not increase count${NC}"
ALL_PASSED=false
fi
if [ "$LOADED_COUNT_3" -le "$LOADED_COUNT_2" ]; then
echo "${RED}✗ FAIL: Step 4 did not increase count${NC}"
ALL_PASSED=false
fi
if [ "$ALL_PASSED" = true ]; then
echo "${GREEN}🎉 ALL TESTS PASSED${NC}"
echo "${GREEN}Progressive on-demand loading verified successfully!${NC}"
EXIT_CODE=0
else
echo "${RED}❌ TESTS FAILED${NC}"
echo "${RED}Progressive loading did not work as expected!${NC}"
EXIT_CODE=1
fi
echo ""
# Restore config
cleanup
if [ -f "$BACKUP_CONFIG" ]; then
mv "$BACKUP_CONFIG" "$CONFIG_FILE"
echo "✓ Restored original config"
fi
echo ""
echo "=========================================="
echo "Test Complete"
echo "=========================================="
echo "Log saved to: /tmp/progressive_test.log"
echo ""
exit $EXIT_CODE

327
custom_nodes/ComfyUI-Impact-Pack/tests/wildcards/test_sequential_loading.sh Normal file
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#!/bin/bash
# Sequential Multi-Stage Wildcard Loading Test
# Tests transitive wildcards that load in multiple sequential stages
# Auto-detect paths
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMPACT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
PORT=8193
CONFIG_FILE="$IMPACT_DIR/impact-pack.ini"
GREEN='\033[0;32m'
RED='\033[0;31m'
BLUE='\033[0;34m'
YELLOW='\033[1;33m'
CYAN='\033[0;36m'
NC='\033[0m'
echo "=========================================="
echo "Sequential Multi-Stage Wildcard Loading Test"
echo "=========================================="
echo ""
# Setup config for full cache mode
cat > "$CONFIG_FILE" << EOF
[default]
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
custom_wildcards = $IMPACT_DIR/custom_wildcards
disable_gpu_opencv = True
wildcard_cache_limit_mb = 50
EOF
echo "Mode: Full cache mode (50MB limit)"
echo ""
# Kill existing servers
pkill -9 -f "python.*main.py" 2>/dev/null || true
sleep 3
# Start server
COMFYUI_DIR="$(cd "$IMPACT_DIR/../.." && pwd)"
cd "$COMFYUI_DIR"
echo "Starting ComfyUI server on port $PORT..."
bash run.sh --listen 127.0.0.1 --port $PORT > /tmp/sequential_test.log 2>&1 &
SERVER_PID=$!
# Wait for server
echo "Waiting 70 seconds for server startup..."
for i in {1..70}; do
sleep 1
if [ $((i % 10)) -eq 0 ]; then
echo " ... $i seconds"
fi
done
# Check server
if ! curl -s http://127.0.0.1:$PORT/ > /dev/null; then
echo "${RED}✗ Server failed to start${NC}"
exit 1
fi
echo "${GREEN}✓ Server started${NC}"
echo ""
# Test function with stage visualization
test_sequential() {
local TEST_NUM=$1
local RAW_PROMPT=$2
local SEED=$3
local DESCRIPTION=$4
local EXPECTED_STAGES=$5 # Number of expected expansion stages
echo "${BLUE}=== Test $TEST_NUM: $DESCRIPTION ===${NC}"
echo "Raw prompt: ${YELLOW}$RAW_PROMPT${NC}"
echo "Seed: $SEED"
echo "Expected stages: $EXPECTED_STAGES"
echo ""
# Test the prompt
RESULT=$(curl -s -X POST http://127.0.0.1:$PORT/impact/wildcards \
-H "Content-Type: application/json" \
-d "{\"text\": \"$RAW_PROMPT\", \"seed\": $SEED}" | \
python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))" 2>/dev/null || echo "ERROR")
echo "${CYAN}Stage Analysis:${NC}"
echo " Stage 0 (Input): $RAW_PROMPT"
# Check if result contains any wildcards (incomplete expansion)
if echo "$RESULT" | grep -q "__.*__"; then
echo " ${YELLOW}⚠ Result still contains wildcards (incomplete expansion)${NC}"
echo " Final Result: $RESULT"
else
echo " ${GREEN}✓ All wildcards fully expanded${NC}"
fi
echo " Final Output: ${GREEN}$RESULT${NC}"
echo ""
# Validate result
if [ "$RESULT" != "ERROR" ] && [ "$RESULT" != "" ]; then
# Check if result still has wildcards (shouldn't have)
if echo "$RESULT" | grep -q "__.*__"; then
echo "Status: ${YELLOW}⚠ PARTIAL - Wildcards remain${NC}"
else
echo "Status: ${GREEN}✅ SUCCESS - Complete expansion${NC}"
fi
else
echo "Status: ${RED}❌ FAILED - Error or empty result${NC}"
fi
echo ""
}
echo "=========================================="
echo "Sequential Loading Test Suite"
echo "=========================================="
echo ""
echo "${CYAN}Test Category 1: Depth Verification${NC}"
echo "Testing different transitive depths with stage tracking"
echo ""
# Test 1: Depth 1 (Direct wildcard)
test_sequential "01" \
"__samples/flower__" \
42 \
"Depth 1 - Direct wildcard (no transitive)" \
1
# Test 2: Depth 2 (One level transitive)
test_sequential "02" \
"__dragon__" \
200 \
"Depth 2 - One level transitive" \
2
# Test 3: Depth 3 (Two levels transitive)
test_sequential "03" \
"__dragon__ warrior" \
200 \
"Depth 3 - Two levels with suffix" \
3
# Test 4: Depth 3 (Maximum verified depth)
test_sequential "04" \
"__adnd__ creature" \
222 \
"Depth 3 - Maximum transitive chain" \
3
echo ""
echo "${CYAN}Test Category 2: Mixed Transitive Scenarios${NC}"
echo "Testing wildcards mixed with dynamic prompts"
echo ""
# Test 5: Transitive with dynamic prompt
test_sequential "05" \
"{__dragon__|__adnd__} in battle" \
100 \
"Dynamic selection of transitive wildcards" \
3
# Test 6: Multiple transitive wildcards
test_sequential "06" \
"__dragon__ fights __adnd__" \
150 \
"Multiple transitive wildcards in one prompt" \
3
# Test 7: Nested transitive in dynamic
test_sequential "07" \
"powerful {__dragon__|__adnd__|simple warrior}" \
200 \
"Transitive wildcards nested in dynamic prompts" \
3
echo ""
echo "${CYAN}Test Category 3: Complex Sequential Scenarios${NC}"
echo "Testing complex multi-stage expansions"
echo ""
# Test 8: Transitive with weights
test_sequential "08" \
"{5::__dragon__|3::__adnd__|regular warrior}" \
250 \
"Weighted selection with transitive wildcards" \
3
# Test 9: Multi-select with transitive
test_sequential "09" \
"{2\$\$, \$\$__dragon__|__adnd__|warrior|mage}" \
300 \
"Multi-select including transitive wildcards" \
3
# Test 10: Quantified transitive
test_sequential "10" \
"{2\$\$, \$\$3#__dragon__}" \
350 \
"Quantified wildcard with transitive expansion" \
3
echo ""
echo "${CYAN}Test Category 4: Edge Cases${NC}"
echo "Testing boundary conditions and special cases"
echo ""
# Test 11: Transitive in compound grammar
test_sequential "11" \
"1{girl holding __samples/flower__|boy riding __dragon__}" \
400 \
"Compound grammar with mixed transitive depths" \
3
# Test 12: Multiple wildcards, different depths
test_sequential "12" \
"__samples/flower__ and __dragon__ with __colors__" \
450 \
"Multiple wildcards with varying depths" \
3
# Test 13: YAML wildcard (no transitive)
test_sequential "13" \
"__colors__" \
333 \
"YAML wildcard (depth 1, no transitive)" \
1
# Test 14: Transitive + YAML combination
test_sequential "14" \
"__dragon__ with __colors__ armor" \
500 \
"Combination of transitive and YAML wildcards" \
3
echo ""
echo "${CYAN}Test Category 5: On-Demand Mode Verification${NC}"
echo "Testing sequential loading in on-demand mode"
echo ""
# Switch to on-demand mode
cat > "$CONFIG_FILE" << EOF
[default]
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
custom_wildcards = $IMPACT_DIR/custom_wildcards
disable_gpu_opencv = True
wildcard_cache_limit_mb = 0.5
EOF
# Restart server
kill $SERVER_PID 2>/dev/null
pkill -9 -f "python.*main.py.*$PORT" 2>/dev/null
sleep 3
echo "Restarting server in on-demand mode (0.5MB limit)..."
cd "$COMFYUI_DIR"
bash run.sh --listen 127.0.0.1 --port $PORT > /tmp/sequential_ondemand.log 2>&1 &
SERVER_PID=$!
echo "Waiting 70 seconds for server restart..."
for i in {1..70}; do
sleep 1
if [ $((i % 10)) -eq 0 ]; then
echo " ... $i seconds"
fi
done
if ! curl -s http://127.0.0.1:$PORT/ > /dev/null; then
echo "${RED}✗ Server failed to restart${NC}"
exit 1
fi
echo "${GREEN}✓ Server restarted in on-demand mode${NC}"
echo ""
# Test 15: Same transitive in on-demand mode
test_sequential "15" \
"__adnd__ creature" \
222 \
"Depth 3 transitive in on-demand mode (should match full cache)" \
3
# Test 16: Complex scenario in on-demand
test_sequential "16" \
"{__dragon__|__adnd__} {warrior|mage}" \
100 \
"Complex transitive with dynamic in on-demand mode" \
3
# Test 17: Multiple transitive in on-demand
test_sequential "17" \
"__dragon__ and __adnd__ together" \
150 \
"Multiple transitive wildcards in on-demand mode" \
3
# Stop server
kill $SERVER_PID 2>/dev/null
pkill -9 -f "python.*main.py.*$PORT" 2>/dev/null
echo "=========================================="
echo "Test Summary"
echo "=========================================="
echo ""
echo "Total tests: 17"
echo "Categories:"
echo " - Depth Verification (4 tests)"
echo " - Mixed Transitive Scenarios (3 tests)"
echo " - Complex Sequential Scenarios (3 tests)"
echo " - Edge Cases (4 tests)"
echo " - On-Demand Mode Verification (3 tests)"
echo ""
echo "Test Focus:"
echo " ✓ Multi-stage transitive wildcard expansion"
echo " ✓ Sequential loading across different depths"
echo " ✓ Transitive wildcards in dynamic prompts"
echo " ✓ Transitive wildcards with weights and multi-select"
echo " ✓ On-demand mode sequential loading verification"
echo ""
echo "Log saved to:"
echo " - Full cache mode: /tmp/sequential_test.log"
echo " - On-demand mode: /tmp/sequential_ondemand.log"
echo ""

281
custom_nodes/ComfyUI-Impact-Pack/tests/wildcards/test_versatile_prompts.sh Executable file
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#!/bin/bash
# Comprehensive wildcard prompt test suite
# Tests all features from ImpactWildcard tutorial
# Auto-detect paths
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMPACT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
PORT=8192
CONFIG_FILE="$IMPACT_DIR/impact-pack.ini"
GREEN='\033[0;32m'
RED='\033[0;31m'
BLUE='\033[0;34m'
YELLOW='\033[1;33m'
NC='\033[0m'
echo "=========================================="
echo "Versatile Wildcard Prompt Test Suite"
echo "=========================================="
echo ""
# Setup config
cat > "$CONFIG_FILE" << EOF
[default]
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
custom_wildcards = $IMPACT_DIR/custom_wildcards
disable_gpu_opencv = True
wildcard_cache_limit_mb = 50
EOF
echo "Mode: Full cache mode (50MB limit)"
echo ""
# Kill existing servers
pkill -9 -f "python.*main.py" 2>/dev/null || true
sleep 3
# Start server
COMFYUI_DIR="$(cd "$IMPACT_DIR/../.." && pwd)"
cd "$COMFYUI_DIR"
echo "Starting ComfyUI server on port $PORT..."
bash run.sh --listen 127.0.0.1 --port $PORT > /tmp/versatile_test.log 2>&1 &
SERVER_PID=$!
# Wait for server
echo "Waiting 70 seconds for server startup..."
for i in {1..70}; do
sleep 1
if [ $((i % 10)) -eq 0 ]; then
echo " ... $i seconds"
fi
done
# Check server
if ! curl -s http://127.0.0.1:$PORT/ > /dev/null; then
echo "${RED}✗ Server failed to start${NC}"
exit 1
fi
echo "${GREEN}✓ Server started${NC}"
echo ""
# Test function
test_prompt() {
local TEST_NUM=$1
local CATEGORY=$2
local PROMPT=$3
local SEED=$4
local DESCRIPTION=$5
echo "${BLUE}=== Test $TEST_NUM: $CATEGORY ===${NC}"
echo "Description: $DESCRIPTION"
echo "Raw prompt: ${YELLOW}$PROMPT${NC}"
echo "Seed: $SEED"
RESULT=$(curl -s -X POST http://127.0.0.1:$PORT/impact/wildcards \
-H "Content-Type: application/json" \
-d "{\"text\": \"$PROMPT\", \"seed\": $SEED}" | \
python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))" 2>/dev/null || echo "ERROR")
echo "Populated: ${GREEN}$RESULT${NC}"
if [ "$RESULT" != "ERROR" ] && [ "$RESULT" != "" ]; then
echo "Status: ${GREEN}✅ SUCCESS${NC}"
else
echo "Status: ${RED}❌ FAILED${NC}"
fi
echo ""
}
echo "=========================================="
echo "Test Suite Execution"
echo "=========================================="
echo ""
# Category 1: Simple Wildcards
test_prompt "01" "Simple Wildcard" \
"__samples/flower__" \
42 \
"Basic wildcard substitution"
test_prompt "02" "Case Insensitive" \
"__SAMPLES/FLOWER__" \
42 \
"Wildcard names are case insensitive"
test_prompt "03" "Mixed Case" \
"__SaMpLeS/FlOwEr__" \
42 \
"Mixed case should work identically"
# Category 2: Dynamic Prompts
test_prompt "04" "Dynamic Prompt (Simple)" \
"{red|green|blue} apple" \
100 \
"Random selection from pipe-separated options"
test_prompt "05" "Dynamic Prompt (Nested)" \
"{a|{d|e|f}|c}" \
100 \
"Nested dynamic prompts with inner choices"
test_prompt "06" "Dynamic Prompt (Complex)" \
"{blue apple|red {cherry|berry}|green melon}" \
100 \
"Nested options with multiple levels"
# Category 3: Selection Weights
test_prompt "07" "Weighted Selection" \
"{5::red|4::green|7::blue|black} car" \
100 \
"Weighted random selection (5:4:7:1 ratio)"
test_prompt "08" "Weighted Complex" \
"A {10::beautiful|5::stunning|amazing} {3::sunset|2::sunrise|dawn}" \
100 \
"Multiple weighted selections in one prompt"
# Category 4: Compound Grammar
test_prompt "09" "Wildcard + Dynamic" \
"1girl holding {blue pencil|red apple|colorful __samples/flower__}" \
100 \
"Mixing wildcard with dynamic prompt"
test_prompt "10" "Multiple Wildcards" \
"__samples/flower__ and __colors__" \
100 \
"Multiple wildcards in single prompt"
test_prompt "11" "Complex Compound" \
"{1girl holding|1boy riding} {blue|red|__colors__} {pencil|__samples/flower__}" \
100 \
"Complex nesting with wildcards and dynamics"
# Category 5: Transitive Wildcards
test_prompt "12" "Transitive Depth 1" \
"__dragon__" \
200 \
"First level transitive wildcard"
test_prompt "13" "Transitive Depth 2" \
"__dragon__ warrior" \
200 \
"Second level transitive with suffix"
test_prompt "14" "Transitive Depth 3" \
"__adnd__ creature" \
222 \
"Third level transitive (adnd→dragon→dragon_spirit)"
# Category 6: Multi-Select
test_prompt "15" "Multi-Select (Fixed)" \
"{2\$\$, \$\$red|green|blue|yellow|purple}" \
100 \
"Select exactly 2 items with comma separator"
test_prompt "16" "Multi-Select (Range)" \
"{1-3\$\$, \$\$apple|banana|orange|grape|mango}" \
100 \
"Select 1-3 items randomly"
test_prompt "17" "Multi-Select (Custom Sep)" \
"{2\$\$ and \$\$cat|dog|bird|fish}" \
100 \
"Custom separator: 'and' instead of comma"
test_prompt "18" "Multi-Select (Or Sep)" \
"{2-3\$\$ or \$\$happy|sad|excited|calm}" \
100 \
"Range with 'or' separator"
# Category 7: Quantifying Wildcard
test_prompt "19" "Quantified Wildcard" \
"{2\$\$, \$\$3#__samples/flower__}" \
100 \
"Repeat wildcard 3 times, select 2"
test_prompt "20" "Quantified Complex" \
"Garden with {3\$\$, \$\$5#__samples/flower__}" \
100 \
"Select 3 from 5 repeated wildcards"
# Category 8: YAML Wildcards
test_prompt "21" "YAML Simple" \
"__colors__" \
333 \
"YAML wildcard file"
test_prompt "22" "YAML in Dynamic" \
"{solid|{metallic|pastel} __colors__}" \
100 \
"YAML wildcard nested in dynamic prompt"
# Category 9: Complex Real-World Scenarios
test_prompt "23" "Realistic Prompt 1" \
"1girl, {5::beautiful|3::stunning|gorgeous} __samples/flower__ in hair, {blue|red|__colors__} dress" \
100 \
"Realistic character description"
test_prompt "24" "Realistic Prompt 2" \
"{detailed|highly detailed} {portrait|illustration} of {1girl|1boy} with {2\$\$, \$\$__samples/flower__|__samples/jewel__|elegant accessories}" \
100 \
"Complex art prompt with multi-select"
test_prompt "25" "Realistic Prompt 3" \
"__adnd__ {warrior|mage|rogue}, {10::epic|5::legendary|mythical} {armor|robes}, wielding {ancient|magical} weapon" \
100 \
"Fantasy character with transitive wildcard"
# Category 10: Edge Cases
test_prompt "26" "Empty Dynamic" \
"{|something|nothing}" \
100 \
"Dynamic with empty option"
test_prompt "27" "Single Option" \
"{only_one}" \
100 \
"Dynamic with single option (no choice)"
test_prompt "28" "Deeply Nested" \
"{a|{b|{c|{d|e}}}}" \
100 \
"Very deep nesting"
test_prompt "29" "Multiple Weights" \
"{100::common|10::uncommon|1::rare|super_rare}" \
100 \
"Extreme weight differences"
test_prompt "30" "Wildcard Only" \
"__samples/flower__" \
999 \
"Different seed on same wildcard"
# Stop server
kill $SERVER_PID 2>/dev/null
pkill -9 -f "python.*main.py.*$PORT" 2>/dev/null
echo "=========================================="
echo "Test Summary"
echo "=========================================="
echo ""
echo "Total tests: 30"
echo "Categories tested:"
echo " - Simple Wildcards (3 tests)"
echo " - Dynamic Prompts (3 tests)"
echo " - Selection Weights (2 tests)"
echo " - Compound Grammar (3 tests)"
echo " - Transitive Wildcards (3 tests)"
echo " - Multi-Select (4 tests)"
echo " - Quantifying Wildcard (2 tests)"
echo " - YAML Wildcards (2 tests)"
echo " - Real-World Scenarios (3 tests)"
echo " - Edge Cases (5 tests)"
echo ""
echo "Log saved to: /tmp/versatile_test.log"
echo ""

226
custom_nodes/ComfyUI-Impact-Pack/tests/wildcards/test_wildcard_consistency.sh Executable file
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#!/bin/bash
# Test wildcard consistency between full cache and on-demand modes
set -e
# Auto-detect paths
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMPACT_PACK_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
COMFYUI_DIR="$(cd "$IMPACT_PACK_DIR/../.." && pwd)"
CONFIG_FILE="$IMPACT_PACK_DIR/impact-pack.ini"
BACKUP_CONFIG="$IMPACT_PACK_DIR/impact-pack.ini.backup"
# Colors
GREEN='\033[0;32m'
RED='\033[0;31m'
BLUE='\033[0;34m'
NC='\033[0m' # No Color
echo "=========================================="
echo "Wildcard Consistency Test"
echo "=========================================="
echo ""
# Backup original config
if [ -f "$CONFIG_FILE" ]; then
cp "$CONFIG_FILE" "$BACKUP_CONFIG"
echo "✓ Backed up original config"
fi
# Function to kill ComfyUI
cleanup() {
pkill -f "python.*main.py" 2>/dev/null || true
sleep 2
}
# Function to test wildcard with specific config
test_with_config() {
local MODE=$1
local CACHE_LIMIT=$2
echo ""
echo "${BLUE}Testing $MODE mode (cache limit: ${CACHE_LIMIT}MB)${NC}"
echo "----------------------------------------"
# Update config
cat > "$CONFIG_FILE" << EOF
[default]
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
custom_wildcards = $IMPACT_PACK_DIR/custom_wildcards
disable_gpu_opencv = True
wildcard_cache_limit_mb = $CACHE_LIMIT
EOF
# Start ComfyUI
cleanup
cd "$COMFYUI_DIR"
bash run.sh --listen 127.0.0.1 --port 8190 > /tmp/comfyui_${MODE}.log 2>&1 &
COMFYUI_PID=$!
echo " Waiting for server startup..."
sleep 15
# Check if server is running
if ! curl -s http://127.0.0.1:8190/ > /dev/null; then
echo "${RED}✗ Server failed to start${NC}"
cat /tmp/comfyui_${MODE}.log | grep -i "wildcard\|error" | tail -20
cleanup
return 1
fi
# Check log for mode
MODE_LOG=$(grep -i "wildcard.*mode" /tmp/comfyui_${MODE}.log | tail -1)
echo " $MODE_LOG"
# Test 1: Simple wildcard
echo ""
echo " Test 1: Simple wildcard substitution"
RESULT1=$(curl -s http://127.0.0.1:8190/impact/wildcards \
-X POST \
-H "Content-Type: application/json" \
-d '{"text": "__samples/flower__", "seed": 42}')
TEXT1=$(echo "$RESULT1" | python3 -c "import sys, json; print(json.load(sys.stdin)['text'])")
echo " Input: __samples/flower__"
echo " Output: $TEXT1"
echo " Result: $RESULT1" > /tmp/result_${MODE}_test1.json
# Test 2: Dynamic prompt
echo ""
echo " Test 2: Dynamic prompt"
RESULT2=$(curl -s http://127.0.0.1:8190/impact/wildcards \
-X POST \
-H "Content-Type: application/json" \
-d '{"text": "{red|blue|green} flower", "seed": 123}')
TEXT2=$(echo "$RESULT2" | python3 -c "import sys, json; print(json.load(sys.stdin)['text'])")
echo " Input: {red|blue|green} flower"
echo " Output: $TEXT2"
echo " Result: $RESULT2" > /tmp/result_${MODE}_test2.json
# Test 3: Combined wildcard and dynamic prompt
echo ""
echo " Test 3: Combined wildcard + dynamic prompt"
RESULT3=$(curl -s http://127.0.0.1:8190/impact/wildcards \
-X POST \
-H "Content-Type: application/json" \
-d '{"text": "beautiful {red|blue} __samples/flower__ with __samples/jewel__", "seed": 456}')
TEXT3=$(echo "$RESULT3" | python3 -c "import sys, json; print(json.load(sys.stdin)['text'])")
echo " Input: beautiful {red|blue} __samples/flower__ with __samples/jewel__"
echo " Output: $TEXT3"
echo " Result: $RESULT3" > /tmp/result_${MODE}_test3.json
# Test 4: Transitive YAML wildcard
echo ""
echo " Test 4: Transitive YAML wildcard (test.yaml)"
RESULT4=$(curl -s http://127.0.0.1:8190/impact/wildcards \
-X POST \
-H "Content-Type: application/json" \
-d '{"text": "__colors__", "seed": 222}')
TEXT4=$(echo "$RESULT4" | python3 -c "import sys, json; print(json.load(sys.stdin)['text'])")
echo " Input: __colors__ (transitive: __cold__|__warm__ -> blue|red|orange|yellow)"
echo " Output: $TEXT4"
echo " Expected: blue|red|orange|yellow"
echo " Result: $RESULT4" > /tmp/result_${MODE}_test4.json
# Test 5: Wildcard list
echo ""
echo " Test 5: Wildcard list API"
LIST_RESULT=$(curl -s http://127.0.0.1:8190/impact/wildcards/list)
LIST_COUNT=$(echo "$LIST_RESULT" | python3 -c "import sys, json; print(len(json.load(sys.stdin)['data']))")
echo " Wildcards found: $LIST_COUNT"
echo " Sample: $(echo "$LIST_RESULT" | python3 -c "import sys, json; print(', '.join(json.load(sys.stdin)['data'][:5]))")"
echo " Result: $LIST_RESULT" > /tmp/result_${MODE}_list.json
# Stop server
cleanup
echo ""
echo "${GREEN}✓ $MODE mode tests completed${NC}"
}
# Run tests
echo ""
echo "Starting consistency tests..."
# Test full cache mode
test_with_config "full_cache" 50
# Test on-demand mode
test_with_config "on_demand" 1
# Compare results
echo ""
echo "=========================================="
echo "Comparing Results"
echo "=========================================="
echo ""
echo "Test 1: Simple wildcard"
DIFF1=$(diff /tmp/result_full_cache_test1.json /tmp/result_on_demand_test1.json || true)
if [ -z "$DIFF1" ]; then
echo "${GREEN}✓ Results match${NC}"
else
echo "${RED}✗ Results differ${NC}"
echo "$DIFF1"
fi
echo ""
echo "Test 2: Dynamic prompt"
DIFF2=$(diff /tmp/result_full_cache_test2.json /tmp/result_on_demand_test2.json || true)
if [ -z "$DIFF2" ]; then
echo "${GREEN}✓ Results match${NC}"
else
echo "${RED}✗ Results differ${NC}"
echo "$DIFF2"
fi
echo ""
echo "Test 3: Combined wildcard + dynamic prompt"
DIFF3=$(diff /tmp/result_full_cache_test3.json /tmp/result_on_demand_test3.json || true)
if [ -z "$DIFF3" ]; then
echo "${GREEN}✓ Results match${NC}"
else
echo "${RED}✗ Results differ${NC}"
echo "$DIFF3"
fi
echo ""
echo "Test 4: Transitive YAML wildcard"
DIFF4=$(diff /tmp/result_full_cache_test4.json /tmp/result_on_demand_test4.json || true)
if [ -z "$DIFF4" ]; then
echo "${GREEN}✓ Results match${NC}"
else
echo "${RED}✗ Results differ${NC}"
echo "$DIFF4"
fi
echo ""
echo "Test 5: Wildcard list"
DIFF_LIST=$(diff /tmp/result_full_cache_list.json /tmp/result_on_demand_list.json || true)
if [ -z "$DIFF_LIST" ]; then
echo "${GREEN}✓ Wildcard lists match${NC}"
else
echo "${RED}✗ Wildcard lists differ${NC}"
echo "$DIFF_LIST"
fi
# Restore original config
if [ -f "$BACKUP_CONFIG" ]; then
mv "$BACKUP_CONFIG" "$CONFIG_FILE"
echo ""
echo "✓ Restored original config"
fi
# Final cleanup
cleanup
echo ""
echo "=========================================="
echo "Consistency Test Complete"
echo "=========================================="

165
custom_nodes/ComfyUI-Impact-Pack/tests/wildcards/test_wildcard_final.py Normal file
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#!/usr/bin/env python3
"""
Final comprehensive wildcard test - validates consistency between full cache and on-demand modes
Tests include:
1. Simple wildcard substitution
2. Nested wildcards (transitive loading)
3. Multiple wildcards in single prompt
4. Dynamic prompts combined with wildcards
5. YAML-based wildcards
"""
import subprocess
import time
import sys
from pathlib import Path
# Auto-detect paths
SCRIPT_DIR = Path(__file__).parent
IMPACT_PACK_DIR = SCRIPT_DIR.parent
COMFYUI_DIR = IMPACT_PACK_DIR.parent.parent
CONFIG_FILE = IMPACT_PACK_DIR / "impact-pack.ini"
def run_test(test_name, cache_limit, test_cases):
"""Run tests with specific cache limit"""
print(f"\n{'='*60}")
print(f"Testing: {test_name}")
print(f"Cache Limit: {cache_limit} MB")
print(f"{'='*60}\n")
# Update config
config_content = f"""[default]
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
custom_wildcards = {IMPACT_PACK_DIR}/custom_wildcards
disable_gpu_opencv = True
wildcard_cache_limit_mb = {cache_limit}
"""
with open(CONFIG_FILE, 'w') as f:
f.write(config_content)
# Start ComfyUI
print("Starting ComfyUI...")
proc = subprocess.Popen(
['bash', 'run.sh', '--listen', '127.0.0.1', '--port', '8191'],
cwd=str(COMFYUI_DIR),
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True
)
# Wait for server to start
time.sleep(20)
# Check logs
import requests
try:
response = requests.get('http://127.0.0.1:8191/')
print("✓ Server started successfully\n")
except Exception:
print("✗ Server failed to start")
proc.terminate()
return {}
# Run test cases
results = {}
for i, (description, text, seed) in enumerate(test_cases, 1):
print(f"Test {i}: {description}")
print(f" Input: {text}")
try:
response = requests.post(
'http://127.0.0.1:8191/impact/wildcards',
json={'text': text, 'seed': seed},
timeout=5
)
result = response.json()
output = result.get('text', '')
print(f" Output: {output}")
results[f"test{i}"] = output
except Exception as e:
print(f" Error: {e}")
results[f"test{i}"] = f"ERROR: {e}"
print()
# Stop server
proc.terminate()
time.sleep(2)
return results
def main():
print("\n" + "="*60)
print("WILDCARD COMPREHENSIVE CONSISTENCY TEST")
print("="*60)
# Test cases: (description, wildcard text, seed)
test_cases = [
# Test 1: Simple wildcard
("Simple wildcard", "__samples/flower__", 42),
# Test 2: Multiple wildcards
("Multiple wildcards", "a __samples/flower__ and a __samples/jewel__", 123),
# Test 3: Dynamic prompt
("Dynamic prompt", "{red|blue|green} flower", 456),
# Test 4: Combined wildcard + dynamic
("Combined", "{beautiful|elegant} __samples/flower__ with {gold|silver} __samples/jewel__", 789),
# Test 5: Nested selection (multi-select)
("Multi-select", "{2$$, $$__samples/flower__|rose|tulip|daisy}", 111),
# Test 6: Transitive YAML wildcard (custom_wildcards/test.yaml)
# __colors__ → __cold__|__warm__ → blue|red|orange|yellow
("Transitive YAML wildcard", "__colors__", 222),
# Test 7: Transitive with text
("Transitive with context", "a {beautiful|vibrant} __colors__ flower", 333),
]
# Test with full cache mode
results_full = run_test("Full Cache Mode", 50, test_cases)
time.sleep(5)
# Test with on-demand mode
results_on_demand = run_test("On-Demand Mode", 1, test_cases)
# Compare results
print("\n" + "="*60)
print("RESULTS COMPARISON")
print("="*60 + "\n")
all_match = True
for key in results_full.keys():
full_result = results_full.get(key, "MISSING")
on_demand_result = results_on_demand.get(key, "MISSING")
match = full_result == on_demand_result
all_match = all_match and match
status = "✓ MATCH" if match else "✗ DIFFER"
print(f"{key}: {status}")
if not match:
print(f" Full cache: {full_result}")
print(f" On-demand: {on_demand_result}")
print()
# Final verdict
print("="*60)
if all_match:
print("✅ ALL TESTS PASSED - Results are identical")
print("="*60)
return 0
else:
print("❌ TESTS FAILED - Results differ between modes")
print("="*60)
return 1
if __name__ == "__main__":
sys.exit(main())

200
custom_nodes/ComfyUI-Impact-Pack/tests/wildcards/test_wildcard_lazy_loading.py Executable file
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#!/usr/bin/env python3
"""
Test script for wildcard lazy loading functionality
"""
import sys
import os
import tempfile
# Add parent directory to path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..'))
from modules.impact import wildcards
def test_lazy_loader():
"""Test LazyWildcardLoader class"""
print("=" * 60)
print("TEST 1: LazyWildcardLoader functionality")
print("=" * 60)
# Create a temporary test file
with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as f:
f.write("option1\n")
f.write("option2\n")
f.write("# comment line\n")
f.write("option3\n")
temp_file = f.name
try:
# Test lazy loading
loader = wildcards.LazyWildcardLoader(temp_file, 'txt')
print(f"✓ Created LazyWildcardLoader: {loader}")
# Check that data is not loaded yet
assert not loader._loaded, "Data should not be loaded initially"
print("✓ Data not loaded initially (lazy)")
# Access data
data = loader.get_data()
print(f"✓ Loaded data: {data}")
assert len(data) == 3, f"Expected 3 items, got {len(data)}"
assert 'option1' in data, "option1 should be in data"
# Check that data is now loaded
assert loader._loaded, "Data should be loaded after access"
print("✓ Data loaded after first access")
# Test list-like operations
print(f"✓ len(loader) = {len(loader)}")
assert len(loader) == 3
print(f"✓ loader[0] = {loader[0]}")
assert loader[0] == 'option1'
print(f"✓ 'option2' in loader = {'option2' in loader}")
assert 'option2' in loader
print(f"✓ list(loader) = {list(loader)}")
print("\n✅ LazyWildcardLoader tests PASSED\n")
finally:
os.unlink(temp_file)
def test_cache_limit_detection():
"""Test automatic cache mode detection"""
print("=" * 60)
print("TEST 2: Cache limit detection")
print("=" * 60)
# Get current cache limit
limit = wildcards.get_cache_limit()
print(f"✓ Cache limit: {limit / (1024*1024):.2f} MB")
# Calculate wildcard directory size
wildcards_dir = wildcards.wildcards_path
total_size = wildcards.calculate_directory_size(wildcards_dir)
print(f"✓ Wildcards directory size: {total_size / (1024*1024):.2f} MB")
print(f"✓ Wildcards path: {wildcards_dir}")
# Determine expected mode
if total_size >= limit:
expected_mode = "on-demand"
else:
expected_mode = "full cache"
print(f"✓ Expected mode: {expected_mode}")
print("\n✅ Cache detection tests PASSED\n")
def test_wildcard_loading():
"""Test actual wildcard loading"""
print("=" * 60)
print("TEST 3: Wildcard loading with current mode")
print("=" * 60)
# Clear existing wildcards
wildcards.wildcard_dict = {}
wildcards._on_demand_mode = False
# Load wildcards
print("Loading wildcards...")
wildcards.wildcard_load()
# Check mode
is_on_demand = wildcards.is_on_demand_mode()
print(f"✓ On-demand mode active: {is_on_demand}")
# Check loaded wildcards
wc_list = wildcards.get_wildcard_list()
print(f"✓ Loaded {len(wc_list)} wildcards")
if len(wc_list) > 0:
print(f"✓ Sample wildcards: {wc_list[:5]}")
# Test accessing a wildcard
if len(wildcards.wildcard_dict) > 0:
key = list(wildcards.wildcard_dict.keys())[0]
value = wildcards.wildcard_dict[key]
print(f"✓ Sample wildcard '{key}' type: {type(value).__name__}")
if isinstance(value, wildcards.LazyWildcardLoader):
print(f" - LazyWildcardLoader: {value}")
print(f" - Loaded: {value._loaded}")
# Access the data
data = value.get_data()
print(f" - Data loaded, items: {len(data)}")
else:
print(f" - Direct list, items: {len(value)}")
print("\n✅ Wildcard loading tests PASSED\n")
def test_on_demand_simulation():
"""Simulate on-demand mode with temporary wildcards"""
print("=" * 60)
print("TEST 4: On-demand mode simulation")
print("=" * 60)
# Create temporary wildcard directory
with tempfile.TemporaryDirectory() as tmpdir:
# Create test files
test_file1 = os.path.join(tmpdir, "test1.txt")
test_file2 = os.path.join(tmpdir, "test2.txt")
with open(test_file1, 'w') as f:
f.write("option1a\noption1b\noption1c\n")
with open(test_file2, 'w') as f:
f.write("option2a\noption2b\n")
# Clear and load with on-demand mode
wildcards.wildcard_dict = {}
wildcards._on_demand_mode = False
print(f"✓ Loading from temp directory: {tmpdir}")
wildcards.read_wildcard_dict(tmpdir, on_demand=True)
print(f"✓ Loaded {len(wildcards.wildcard_dict)} wildcards")
for key, value in wildcards.wildcard_dict.items():
print(f"✓ Wildcard '{key}':")
print(f" - Type: {type(value).__name__}")
if isinstance(value, wildcards.LazyWildcardLoader):
print(f" - Initially loaded: {value._loaded}")
data = value.get_data()
print(f" - After access: loaded={value._loaded}, items={len(data)}")
print(f" - Sample data: {data[:2]}")
print("\n✅ On-demand simulation tests PASSED\n")
def main():
"""Run all tests"""
print("\n" + "=" * 60)
print("WILDCARD LAZY LOADING TEST SUITE")
print("=" * 60 + "\n")
try:
test_lazy_loader()
test_cache_limit_detection()
test_wildcard_loading()
test_on_demand_simulation()
print("=" * 60)
print("✅ ALL TESTS PASSED")
print("=" * 60)
return 0
except Exception as e:
print("\n" + "=" * 60)
print(f"❌ TEST FAILED: {e}")
print("=" * 60)
import traceback
traceback.print_exc()
return 1
if __name__ == "__main__":
sys.exit(main())

97
custom_nodes/ComfyUI-Impact-Pack/tests/wildcards/verify_ondemand_mode.sh Executable file
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#!/bin/bash
# Verify that on-demand mode is actually triggered with 0.5MB limit
# Auto-detect paths
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMPACT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
CONFIG_FILE="$IMPACT_DIR/impact-pack.ini"
echo "=========================================="
echo "Verify On-Demand Mode Activation"
echo "=========================================="
echo ""
# Set config to 0.5MB limit
cat > "$CONFIG_FILE" << EOF
[default]
dependency_version = 24
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_h_4b8939.pth
custom_wildcards = $IMPACT_DIR/custom_wildcards
disable_gpu_opencv = True
wildcard_cache_limit_mb = 0.5
EOF
echo "Config set to 0.5MB cache limit"
echo ""
# Kill any existing servers
pkill -9 -f "python.*main.py" 2>/dev/null || true
sleep 3
# Start server
COMFYUI_DIR="$(cd "$IMPACT_DIR/../.." && pwd)"
cd "$COMFYUI_DIR"
echo "Starting ComfyUI server on port 8190..."
bash run.sh --listen 127.0.0.1 --port 8190 > /tmp/verify_ondemand.log 2>&1 &
SERVER_PID=$!
# Wait for server
echo "Waiting 70 seconds for server startup..."
for i in {1..70}; do
sleep 1
if [ $((i % 10)) -eq 0 ]; then
echo " ... $i seconds"
fi
done
# Check server
if ! curl -s http://127.0.0.1:8190/ > /dev/null; then
echo "✗ Server failed to start"
cat /tmp/verify_ondemand.log
exit 1
fi
echo "✓ Server started"
echo ""
# Check loading mode
echo "Loading mode detected:"
grep -i "wildcard.*mode\|wildcard.*size.*cache" /tmp/verify_ondemand.log | grep -v "Maximum depth"
echo ""
# Verify mode
if grep -q "Using on-demand loading mode" /tmp/verify_ondemand.log; then
echo "✅ SUCCESS: On-demand mode activated with 0.5MB limit!"
elif grep -q "Using full cache mode" /tmp/verify_ondemand.log; then
echo "❌ FAIL: Full cache mode used (should be on-demand)"
echo ""
echo "Cache limit in log:"
grep "cache limit" /tmp/verify_ondemand.log
else
echo "⚠️ WARNING: Could not determine mode"
fi
# Test wildcard functionality
echo ""
echo "Testing wildcard functionality in on-demand mode..."
curl -s -X POST http://127.0.0.1:8190/impact/wildcards \
-H "Content-Type: application/json" \
-d '{"text": "__adnd__ creature", "seed": 222}' > /tmp/verify_result.json
RESULT=$(cat /tmp/verify_result.json | python3 -c "import sys, json; print(json.load(sys.stdin).get('text','ERROR'))" 2>/dev/null || echo "ERROR")
echo " Depth 3 transitive (seed=222): $RESULT"
if [ "$RESULT" = "Shrewd Hatchling creature" ]; then
echo " ✅ Transitive wildcard works correctly"
else
echo " ❌ Unexpected result: $RESULT"
fi
# Stop server
kill $SERVER_PID 2>/dev/null
pkill -9 -f "python.*main.py.*8190" 2>/dev/null
echo ""
echo "Full log saved to: /tmp/verify_ondemand.log"

976
custom_nodes/ComfyUI-Impact-Pack/tests/workflows/advanced-sampler.json Normal file
View File

@@ -0,0 +1,976 @@
{
"last_node_id": 27,
"last_link_id": 46,
"nodes": [
{
"id": 11,
"type": "EditBasicPipe",
"pos": [
1260,
590
],
"size": {
"0": 267,
"1": 126
},
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
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{
"name": "images",
"type": "IMAGE",
"link": 8
}
],
"properties": {
"Node name for S&R": "PreviewImage"
}
},
{
"id": 9,
"type": "PreviewImage",
"pos": [
2720,
680
],
"size": {
"0": 210,
"1": 246
},
"flags": {},
"order": 13,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 50
}
],
"properties": {
"Node name for S&R": "PreviewImage"
}
},
{
"id": 38,
"type": "Note",
"pos": [
2032,
698
],
"size": [
210,
81.49969482421875
],
"flags": {},
"order": 2,
"mode": 0,
"properties": {
"text": ""
},
"widgets_values": [
"MasksToMaskList node introduced\n"
],
"color": "#432",
"bgcolor": "#653"
},
{
"id": 37,
"type": "Note",
"pos": [
2071,
384
],
"size": [
281.500244140625,
65.09967041015625
],
"flags": {},
"order": 3,
"mode": 0,
"properties": {
"text": ""
},
"widgets_values": [
"type of batch_masks => MASKS instead of MASK\n"
],
"color": "#432",
"bgcolor": "#653"
}
],
"links": [
[
5,
4,
1,
5,
0,
"MASK"
],
[
8,
6,
0,
7,
0,
"IMAGE"
],
[
26,
4,
0,
21,
1,
"IMAGE"
],
[
27,
21,
0,
22,
0,
"IMAGE"
],
[
35,
5,
0,
28,
0,
"SEGS"
],
[
41,
28,
0,
21,
0,
"SEGS"
],
[
43,
33,
1,
28,
1,
"MASKS"
],
[
44,
33,
0,
6,
0,
"MASK"
],
[
45,
2,
0,
33,
0,
"SAM_MODEL"
],
[
46,
5,
0,
33,
1,
"SEGS"
],
[
47,
4,
0,
33,
2,
"IMAGE"
],
[
50,
35,
0,
9,
0,
"IMAGE"
],
[
51,
33,
1,
36,
0,
"MASKS"
],
[
52,
36,
0,
35,
0,
"MASK"
]
],
"groups": [],
"config": {},
"extra": {},
"version": 0.4
}

1625
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72
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## When a permission error occurs during the installation process (on Windows)
* There are cases where the package you are trying to install is already being used by another custom node that has been loaded.
* This issue occurs only on Windows.
* Please close ComfyUI and execute install.py directly using Python in the custom_nodes/ComfyUI-Impact-Pack directory.
* In case **portable** version:
1. goto **ComfyUI_windows_portable** directory in **cmd**
2. execute ```.\python_embeded\python -s -m custom_nodes\ComfyUI-Impact-Pack\install.py```
* In case **venv**:
1. activate venv
2. execute ```python -s -m custom_nodes\ComfyUI-Impact-Pack\install.py```
* Others:
1. Please modify the path of 'python' according to your Python environment.
2. execute ```(YOUR PYTHON) -s -m custom_nodes\ComfyUI-Impact-Pack\install.py```
## If the nodes of the Impact Pack hang during execution
* During the execution of processes related to dilation, issues like this may arise depending on the compatibility of the computer environment.
* Please set `disable_gpu_opencv = True` in the `ComfyUI-Impact-Pack/impact-pack.ini` file. Occasionally, issues may arise when the OpenCV GPU mode is activated depending on the environment.
e.g.
```
[default]
dependency_version = 17
mmdet_skip = True
sam_editor_cpu = False
sam_editor_model = sam_vit_b_01ec64.pth
custom_wildcards = /home/me/github/ComfyUI/custom_nodes/ComfyUI-Impact-Pack/custom_wildcards
disable_gpu_opencv = True
```
## An issue has occurred with importing Ultralytics.
```
AttributeError: 'Logger' object has no attribute 'reconfigure'
or
AttributeError: 'Logger' object has no attribute 'encoding'
```
* Update `ComfyUI-Manager` to V1.1.2 or above
## An issue has occurred about 'cv2'
```
AttributeError: module 'cv2' has no attribute 'setNumThreads'
```
* Update 'opencv-python' and 'opencv-python-headless' to latest version
* Once you update to the latest version, you can also downgrade back to 4.6.0.66 if needed.
* For the portable version, navigate to the portable installation directory in the command prompt, and enter the following command:
```
.\python_embeded\python.exe -m pip install -U opencv-python opencv-python-headless
```
* When using the WAS node suite or reactor nodes, using the latest version may not work as expected. You can downgrade using the following command:
```
.\python_embeded\python.exe -m pip install -U opencv-python==4.6.0.66 opencv-python-headless==4.6.0.66
```
## Destortion on Detailer
* Please also note that this issue may be caused by a bug in xformers 0.0.18. If you encounter this problem, please try adjusting the guide_size parameter.
![example](black1.png)
![example](black2.png)
* guide_size changed from 256 -> 192

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