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comfyui-impact-pack/.github/workflows/publish.yml
Vendored
-25
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name: Publish to Comfy registry
on:
workflow_dispatch:
push:
branches:
- main
paths:
- "pyproject.toml"
permissions:
issues: write
jobs:
publish-node:
name: Publish Custom Node to registry
runs-on: ubuntu-latest
if: ${{ github.repository_owner == 'ltdrdata' }}
steps:
- name: Check out code
uses: actions/checkout@v4
- name: Publish Custom Node
uses: Comfy-Org/publish-node-action@v1
with:
## Add your own personal access token to your Github Repository secrets and reference it here.
personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
comfyui-impact-pack/.gitignore
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__pycache__
*.ini
wildcards/**
.vscode/
.idea/
subpack
impact_subpack
*.txt
*.yaml
!requirements.txt
!LICENSE.txt
comfyui-impact-pack/.gitmodules
-3
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@@ -1,3 +0,0 @@
[submodule "subpack"]
path = subpack
url = https://github.com/ltdrdata/ComfyUI-Impact-Subpack
comfyui-impact-pack/.tracking
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.github/workflows/publish.yml
.gitignore
.gitmodules
LICENSE.txt
README.md
__init__.py
custom_wildcards/put_wildcards_here
example_workflows/1-FaceDetailer.jpg
example_workflows/1-FaceDetailer.json
example_workflows/2-MaskDetailer.jpg
example_workflows/2-MaskDetailer.json
example_workflows/3-SEGSDetailer.jpg
example_workflows/3-SEGSDetailer.json
example_workflows/4-MakeTileSEGS-Upscale.jpg
example_workflows/4-MakeTileSEGS-Upscale.json
example_workflows/5-PreviewDetailerHookProvider.jpg
example_workflows/5-PreviewDetailerHookProvider.json
example_workflows/5-prompt-per-tile.jpg
example_workflows/5-prompt-per-tile.json
example_workflows/6-DetailerWildcard.jpg
example_workflows/6-DetailerWildcard.json
install.py
js/common.js
js/impact-image-util.js
js/impact-pack.js
js/impact-sam-editor.js
js/impact-segs-picker.js
js/mask-rect-area-advanced.js
js/mask-rect-area.js
latent.png
locales/ko/nodeDefs.json
modules/impact/additional_dependencies.py
modules/impact/animatediff_nodes.py
modules/impact/bridge_nodes.py
modules/impact/config.py
modules/impact/core.py
modules/impact/defs.py
modules/impact/detectors.py
modules/impact/hf_nodes.py
modules/impact/hook_nodes.py
modules/impact/hooks.py
modules/impact/impact_onnx.py
modules/impact/impact_pack.py
modules/impact/impact_sampling.py
modules/impact/impact_server.py
modules/impact/logics.py
modules/impact/pipe.py
modules/impact/segs_nodes.py
modules/impact/segs_upscaler.py
modules/impact/special_samplers.py
modules/impact/util_nodes.py
modules/impact/utils.py
modules/impact/wildcards.py
modules/thirdparty/noise_nodes.py
node_list.json
notebook/comfyui_colab_impact_pack.ipynb
pyproject.toml
requirements.txt
ruff.toml
test/advanced-sampler.json
test/detailer-pipe-test-sdxl.json
test/detailer-pipe-test.json
test/impactwildcardprocessor_separate_tests.json
test/impactwildcardprocessor_yaml_tests.json
test/loop-test.json
test/masks.json
test/regional_prompt.json
troubleshooting/TROUBLESHOOTING.md
troubleshooting/black1.png
troubleshooting/black2.png
wildcards/put_wildcards_here
wildcards/samples/flower.txt
wildcards/samples/jewel.txt
comfyui-impact-pack/LICENSE.txt
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[![Youtube Badge](https://img.shields.io/badge/Youtube-FF0000?style=for-the-badge&logo=Youtube&logoColor=white&link=https://www.youtube.com/watch?v=AccoxDZIg3Y&list=PL_Ej2RDzjQLGfEeizq4GISeY3FtVyFmGP)](https://www.youtube.com/watch?v=AccoxDZIg3Y&list=PL_Ej2RDzjQLGfEeizq4GISeY3FtVyFmGP)
# 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
* V8.19: 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)).
### 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`.
comfyui-impact-pack/__init__.py
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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']
comfyui-impact-pack/custom_wildcards/put_wildcards_here
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comfyui-impact-pack/disable.py
-38
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@@ -1,38 +0,0 @@
import os
import sys
import time
import platform
import shutil
import subprocess
comfy_path = '../..'
def rmtree(path):
retry_count = 3
while True:
try:
retry_count -= 1
if platform.system() == "Windows":
subprocess.check_call(['attrib', '-R', path + '\\*', '/S'])
shutil.rmtree(path)
return True
except Exception as ex:
print(f"ex: {ex}")
time.sleep(3)
if retry_count < 0:
raise ex
print(f"Uninstall retry({retry_count})")
js_dest_path = os.path.join(comfy_path, "web", "extensions", "impact-pack")
if os.path.exists(js_dest_path):
rmtree(js_dest_path)
comfyui-impact-pack/example_workflows/1-FaceDetailer.jpg
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comfyui-impact-pack/example_workflows/1-FaceDetailer.json
-1269
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comfyui-impact-pack/example_workflows/2-MaskDetailer.json
-596
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@@ -1,596 +0,0 @@
{
"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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comfyui-impact-pack/example_workflows/6-DetailerWildcard.json
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comfyui-impact-pack/install.py
-116
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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()
comfyui-impact-pack/js/common.js
-137
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@@ -1,137 +0,0 @@
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=${Date.now()}`;
let node = app.graph._nodes_by_id[node_id];
if(node)
node.imgs = [img];
}
api.addEventListener("impact-preview", refreshPreview);
comfyui-impact-pack/js/impact-image-util.js
-229
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import { ComfyApp, app } from "../../scripts/app.js";
import { api } from "../../scripts/api.js";
function load_image(str) {
let base64String = canvas.toDataURL('image/png');
let img = new Image();
img.src = base64String;
}
function getFileItem(baseType, path) {
try {
let pathType = baseType;
if (path.endsWith("[output]")) {
pathType = "output";
path = path.slice(0, -9);
} else if (path.endsWith("[input]")) {
pathType = "input";
path = path.slice(0, -8);
} else if (path.endsWith("[temp]")) {
pathType = "temp";
path = path.slice(0, -7);
}
const subfolder = path.substring(0, path.lastIndexOf('/'));
const filename = path.substring(path.lastIndexOf('/') + 1);
return {
filename: filename,
subfolder: subfolder,
type: pathType
};
}
catch(exception) {
return null;
}
}
async function loadImageFromUrl(image, node_id, v, need_to_load) {
let item = getFileItem('temp', v);
if(item) {
let params = `?node_id=${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;
}
});
}
}
})
comfyui-impact-pack/js/impact-pack.js
-933
View File
@@ -1,933 +0,0 @@
import { ComfyApp, app } from "../../scripts/app.js";
import { ComfyDialog, $el } from "../../scripts/ui.js";
import { api } from "../../scripts/api.js";
import { customAlert, isBeforeFrontendVersion } from "./common.js";
const is_legacy_front = () => isBeforeFrontendVersion('1.16.9');
if(is_legacy_front()) {
customAlert("An outdated version(<1.16.9) of the `comfyui-frontend-package` is installed. It is not compatible with the current version of the Impact Pack.");
}
let wildcards_list = [];
async function load_wildcards() {
let res = await api.fetchApi('/impact/wildcards/list');
let data = await res.json();
wildcards_list = data.data;
}
load_wildcards();
export function get_wildcards_list() {
return wildcards_list;
}
// 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);
app.registerExtension({
name: "Comfy.Impack",
commands: [
{
id: 'refresh-impact-wildcard',
label: 'Impact: Refresh Wildcard',
function: async () => {
await api.fetchApi('/impact/wildcards/refresh');
await load_wildcards();
app.extensionManager.toast.add({
severity: 'info',
summary: 'Refreshed!',
detail: 'Impact Wildcard List is refreshed!!',
life: 3000
});
}
}
],
menuCommands: [
{
path: ['Edit'],
commands: ['refresh-impact-wildcard']
}
],
loadedGraphNode(node, app) {
if (node.comfyClass == "MaskPainter") {
input_dirty[node.id + ""] = true;
}
},
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 = (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;
}
}
Object.defineProperty(node.widgets[combo_id+1], "value", {
set: (value) => {
if (value !== "Select the Wildcard to add to the text")
node._wildcard_value = value;
},
get: () => { return "Select the Wildcard to add to the text"; }
});
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;
}
}
});
}
}
});
comfyui-impact-pack/js/impact-sam-editor.js
-641
View File
@@ -1,641 +0,0 @@
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();
},
});
});
}
}
});
comfyui-impact-pack/js/impact-segs-picker.js
-182
View File
@@ -1,182 +0,0 @@
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);
});
}
}
});
comfyui-impact-pack/js/mask-rect-area-advanced.js
-381
View File
@@ -1,381 +0,0 @@
import { app } from "../../scripts/app.js";
function showPreviewCanvas(node, app) {
const widget = {
type: "customCanvas",
name: "mask-rect-area-canvas",
get value() {
return this.canvas.value;
},
set value(x) {
this.canvas.value = x;
},
draw: function (ctx, node, widgetWidth, widgetY) {
// If we are initially offscreen when created we wont have received a resize event
// Calculate it here instead
if (!node.canvasHeight) {
computeCanvasSize(node, node.size);
}
const visible = true;
const t = ctx.getTransform();
const margin = 12;
const border = 2;
const widgetHeight = node.canvasHeight;
const width = Math.round(node.properties["width"]);
const height = Math.round(node.properties["height"]);
const scale = Math.min((widgetWidth - margin * 3) / width, (widgetHeight - margin * 3) / height);
const blurRadius = node.properties["blur_radius"] || 0;
const index = 0;
Object.assign(this.canvas.style, {
left: `${t.e}px`,
top: `${t.f + (widgetY * t.d)}px`,
width: `${widgetWidth * t.a}px`,
height: `${widgetHeight * t.d}px`,
position: "absolute",
zIndex: 1,
fontSize: `${t.d * 10.0}px`,
pointerEvents: "none"
});
this.canvas.hidden = !visible;
let backgroundWidth = width * scale;
let backgroundHeight = height * scale;
let xOffset = margin;
if (backgroundWidth < widgetWidth) {
xOffset += (widgetWidth - backgroundWidth) / 2 - margin;
}
let yOffset = (margin / 2);
if (backgroundHeight < widgetHeight) {
yOffset += (widgetHeight - backgroundHeight) / 2 - margin;
}
let widgetX = xOffset;
widgetY = widgetY + yOffset;
// Draw the background border
ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR;
ctx.fillRect(widgetX - border, widgetY - border, backgroundWidth + border * 2, backgroundHeight + border * 2)
// Draw the main background area
ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR;
ctx.fillRect(widgetX, widgetY, backgroundWidth, backgroundHeight);
// Draw the conditioning zone
let [x, y, w, h] = getDrawArea(node, backgroundWidth, backgroundHeight);
ctx.fillStyle = getDrawColor(0, "80");
ctx.fillRect(widgetX + x, widgetY + y, w, h);
ctx.beginPath();
ctx.lineWidth = 1;
// Draw grid lines
for (let x = 0; x <= width / 64; x += 1) {
ctx.moveTo(widgetX + x * 64 * scale, widgetY);
ctx.lineTo(widgetX + x * 64 * scale, widgetY + backgroundHeight);
}
for (let y = 0; y <= height / 64; y += 1) {
ctx.moveTo(widgetX, widgetY + y * 64 * scale);
ctx.lineTo(widgetX + backgroundWidth, widgetY + y * 64 * scale);
}
ctx.strokeStyle = "#66666650";
ctx.stroke();
ctx.closePath();
// Draw current zone
let [sx, sy, sw, sh] = getDrawArea(node, backgroundWidth, backgroundHeight);
ctx.fillStyle = getDrawColor(0, "80");
ctx.fillRect(widgetX + sx, widgetY + sy, sw, sh);
ctx.fillStyle = getDrawColor(0, "40");
ctx.fillRect(widgetX + sx + border, widgetY + sy + border, sw - border * 2, sh - border * 2);
// Draw white border around the current zone
ctx.strokeStyle = globalThis.LiteGraph.NODE_SELECTED_TITLE_COLOR;
ctx.lineWidth = 2;
ctx.strokeRect(widgetX + sx, widgetY + sy, sw, sh);
// Display
ctx.beginPath();
ctx.arc(LiteGraph.NODE_SLOT_HEIGHT * 0.5, LiteGraph.NODE_SLOT_HEIGHT * (index + 0.5) + 4, 4, 0, Math.PI * 2);
ctx.fill();
ctx.lineWidth = 1;
ctx.strokeStyle = "white";
ctx.stroke();
ctx.lineWidth = 1;
ctx.closePath();
// Draw progress bar canvas
if (backgroundWidth < widgetWidth) {
xOffset += (widgetWidth - backgroundWidth) / 2 - margin;
}
// Ajustar las coordenadas X e Y
const barHeight = 8;
let widgetYBar = widgetY + backgroundHeight + margin;
// Dibujar el borde negro alrededor de la barra
ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR;
ctx.fillRect(
widgetX - border,
widgetYBar - border,
backgroundWidth + border * 2,
barHeight + border * 2
);
// Dibujar el área principal de la barra (fondo)
ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR; // Mismo color de fondo que el canvas
ctx.fillRect(
widgetX,
widgetYBar,
backgroundWidth,
barHeight
);
// Draw progress bar grid
ctx.beginPath();
ctx.lineWidth = 1;
ctx.strokeStyle = "#66666650";
// Calcular el número de líneas en función del tamaño de la barra
const numLines = Math.floor(backgroundWidth / 64);
// Dibujar líneas del grid
for (let x = 0; x <= width / 64; x += 1) {
ctx.moveTo(widgetX + x * 64 * scale, widgetYBar);
ctx.lineTo(widgetX + x * 64 * scale, widgetYBar + barHeight);
}
ctx.stroke();
ctx.closePath();
// Dibujar progreso (basado en blur_radius)
const progress = Math.min(blurRadius / 255, 1);
ctx.fillStyle = "rgba(0, 120, 255, 0.5)";
ctx.fillRect(
widgetX,
widgetYBar,
backgroundWidth * progress,
barHeight
);
}
};
widget.canvas = document.createElement("canvas");
widget.canvas.className = "mask-rect-area-canvas";
widget.parent = node;
document.body.appendChild(widget.canvas);
node.addCustomWidget(widget);
app.canvas.onDrawBackground = function () {
// Draw node isnt fired once the node is off the screen
// if it goes off screen quickly, the input may not be removed
// this shifts it off screen so it can be moved back if the node is visible.
for (let n in app.graph._nodes) {
n = app.graph._nodes[n];
for (let w in n.widgets) {
let wid = n.widgets[w];
if (Object.hasOwn(wid, "canvas")) {
wid.canvas.style.left = -8000 + "px";
wid.canvas.style.position = "absolute";
}
}
}
};
node.onResize = function (size) {
computeCanvasSize(node, size);
};
return {minWidth: 200, minHeight: 200, widget};
}
app.registerExtension({
name: 'drltdata.MaskRectAreaAdvanced',
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (nodeData.name === "MaskRectAreaAdvanced") {
const onNodeCreated = nodeType.prototype.onNodeCreated;
nodeType.prototype.onNodeCreated = function () {
const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined;
this.setProperty("width", 512);
this.setProperty("height", 512);
this.setProperty("x", 0);
this.setProperty("y", 0);
this.setProperty("w", 256);
this.setProperty("h", 256);
this.setProperty("blur_radius", 0);
this.selected = false;
this.index = 3;
this.serialize_widgets = true;
CUSTOM_INT(this, "x", 0, function (v, _, node) {
const s = this.options.step / 10;
this.value = Math.round(v / s) * s;
node.properties["x"] = this.value;
});
CUSTOM_INT(this, "y", 0, function (v, _, node) {
const s = this.options.step / 10;
this.value = Math.round(v / s) * s;
node.properties["y"] = this.value;
});
CUSTOM_INT(this, "width", 256, function (v, _, node) {
const s = this.options.step / 10;
this.value = Math.round(v / s) * s;
node.properties["w"] = this.value;
});
CUSTOM_INT(this, "height", 256, function (v, _, node) {
const s = this.options.step / 10;
this.value = Math.round(v / s) * s;
node.properties["h"] = this.value;
});
CUSTOM_INT(this, "image_width", 512, function (v, _, node) {
const s = this.options.step / 10;
this.value = Math.round(v / s) * s;
node.properties["width"] = this.value;
});
CUSTOM_INT(this, "image_height", 512, function (v, _, node) {
const s = this.options.step / 10;
this.value = Math.round(v / s) * s;
node.properties["height"] = this.value;
});
CUSTOM_INT(this, "blur_radius", 0, function (v, _, node) {
this.value = Math.round(v) || 0;
node.properties["blur_radius"] = this.value;
},
{"min": 0, "max": 255, "step": 10}
);
showPreviewCanvas(this, app);
this.onSelected = function () {
this.selected = true;
};
this.onDeselected = function () {
this.selected = false;
};
return r;
};
}
}
});
// Calculate the drawing area using individual properties.
function getDrawArea(node, backgroundWidth, backgroundHeight) {
let x = node.properties["x"] * backgroundWidth / node.properties["width"];
let y = node.properties["y"] * backgroundHeight / node.properties["height"];
let w = node.properties["w"] * backgroundWidth / node.properties["width"];
let h = node.properties["h"] * backgroundHeight / node.properties["height"];
if (x > backgroundWidth) {
x = backgroundWidth;
}
if (y > backgroundHeight) {
y = backgroundHeight;
}
if (x + w > backgroundWidth) {
w = Math.max(0, backgroundWidth - x);
}
if (y + h > backgroundHeight) {
h = Math.max(0, backgroundHeight - y);
}
return [x, y, w, h];
}
function CUSTOM_INT(node, inputName, val, func, config = {}) {
return {
widget: node.addWidget(
"number",
inputName,
val,
func,
Object.assign({}, {min: 0, max: 4096, step: 640, precision: 0}, config)
)
};
}
function getDrawColor(percent, alpha) {
let h = 360 * percent;
let s = 50;
let l = 50;
l /= 100;
const a = s * Math.min(l, 1 - l) / 100;
const f = n => {
const k = (n + h / 30) % 12;
const color = l - a * Math.max(Math.min(k - 3, 9 - k, 1), -1);
return Math.round(255 * color).toString(16).padStart(2, '0'); // convert to Hex and prefix "0" if needed
};
return `#${f(0)}${f(8)}${f(4)}${alpha}`;
}
function computeCanvasSize(node, size) {
if (node.widgets[0].last_y == null) {
return;
}
const MIN_HEIGHT = 220;
const MIN_WIDTH = 240;
let y = LiteGraph.NODE_WIDGET_HEIGHT * Math.max(node.inputs.length, node.outputs.length) + 5;
let freeSpace = size[1] - y;
// Compute the height of all non-customCanvas widgets
let widgetHeight = 0;
for (let i = 0; i < node.widgets.length; i++) {
const w = node.widgets[i];
if (w.type !== "customCanvas") {
if (w.computeSize) {
widgetHeight += w.computeSize()[1] + 4;
} else {
widgetHeight += LiteGraph.NODE_WIDGET_HEIGHT + 5;
}
}
}
// Ensure there is enough vertical space
freeSpace -= widgetHeight;
// Adjust the height of the node if needed
if (freeSpace < MIN_HEIGHT) {
freeSpace = MIN_HEIGHT;
node.size[1] = y + widgetHeight + freeSpace;
node.graph.setDirtyCanvas(true);
}
// Ensure the node width meets the minimum width requirement
if (node.size[0] < MIN_WIDTH) {
node.size[0] = MIN_WIDTH;
node.graph.setDirtyCanvas(true);
}
// Position each of the widgets
for (const w of node.widgets) {
w.y = y;
if (w.type === "customCanvas") {
y += freeSpace;
} else if (w.computeSize) {
y += w.computeSize()[1] + 4;
} else {
y += LiteGraph.NODE_WIDGET_HEIGHT + 4;
}
}
node.canvasHeight = freeSpace;
}
comfyui-impact-pack/js/mask-rect-area.js
-366
View File
@@ -1,366 +0,0 @@
import { app } from "../../scripts/app.js";
function showPreviewCanvas(node, app) {
const widget = {
type: "customCanvas",
name: "mask-rect-area-canvas",
get value() {
return this.canvas.value;
},
set value(x) {
this.canvas.value = x;
},
draw: function (ctx, node, widgetWidth, widgetY) {
// If we are initially offscreen when created we wont have received a resize event
// Calculate it here instead
if (!node.canvasHeight) {
computeCanvasSize(node, node.size);
}
const visible = true;
const t = ctx.getTransform();
const margin = 12;
const border = 2;
const widgetHeight = node.canvasHeight;
const width = 512;
const height = 512;
const scale = Math.min((widgetWidth - margin * 3) / width, (widgetHeight - margin * 3) / height);
const blurRadius = node.properties["blur_radius"] || 0;
const index = 0;
Object.assign(this.canvas.style, {
left: `${t.e}px`,
top: `${t.f + (widgetY * t.d)}px`,
width: `${widgetWidth * t.a}px`,
height: `${widgetHeight * t.d}px`,
position: "absolute",
zIndex: 1,
fontSize: `${t.d * 10.0}px`,
pointerEvents: "none"
});
this.canvas.hidden = !visible;
let backgroundWidth = width * scale;
let backgroundHeight = height * scale;
let xOffset = margin;
if (backgroundWidth < widgetWidth) {
xOffset += (widgetWidth - backgroundWidth) / 2 - margin;
}
let yOffset = (margin / 2);
if (backgroundHeight < widgetHeight) {
yOffset += (widgetHeight - backgroundHeight) / 2 - margin;
}
let widgetX = xOffset;
widgetY = widgetY + yOffset;
// Draw the background border
ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR;
ctx.fillRect(widgetX - border, widgetY - border, backgroundWidth + border * 2, backgroundHeight + border * 2);
// Draw the main background area
ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR;
ctx.fillRect(widgetX, widgetY, backgroundWidth, backgroundHeight);
// Draw the conditioning zone
let [x, y, w, h] = getDrawArea(node, backgroundWidth, backgroundHeight);
ctx.fillStyle = getDrawColor(0, "80");
ctx.fillRect(widgetX + x, widgetY + y, w, h);
ctx.beginPath();
ctx.lineWidth = 1;
// Draw grid lines
for (let x = 0; x <= width / 64; x += 1) {
ctx.moveTo(widgetX + x * 64 * scale, widgetY);
ctx.lineTo(widgetX + x * 64 * scale, widgetY + backgroundHeight);
}
for (let y = 0; y <= height / 64; y += 1) {
ctx.moveTo(widgetX, widgetY + y * 64 * scale);
ctx.lineTo(widgetX + backgroundWidth, widgetY + y * 64 * scale);
}
ctx.strokeStyle = "#66666650";
ctx.stroke();
ctx.closePath();
// Draw current zone
let [sx, sy, sw, sh] = getDrawArea(node, backgroundWidth, backgroundHeight);
ctx.fillStyle = getDrawColor(0, "80");
ctx.fillRect(widgetX + sx, widgetY + sy, sw, sh);
ctx.fillStyle = getDrawColor(0, "40");
ctx.fillRect(widgetX + sx + border, widgetY + sy + border, sw - border * 2, sh - border * 2);
// Draw white border around the current zone
ctx.strokeStyle = globalThis.LiteGraph.NODE_SELECTED_TITLE_COLOR;
ctx.lineWidth = 2;
ctx.strokeRect(widgetX + sx, widgetY + sy, sw, sh);
//ctx.strokeRect(finalSX, finalSY, finalSW, finalSH);
// Display
ctx.beginPath();
ctx.arc(LiteGraph.NODE_SLOT_HEIGHT * 0.5, LiteGraph.NODE_SLOT_HEIGHT * (index + 0.5) + 4, 4, 0, Math.PI * 2);
ctx.fill();
ctx.lineWidth = 1;
ctx.strokeStyle = "white";
ctx.stroke();
ctx.lineWidth = 1;
ctx.closePath();
// Draw progress bar canvas
if (backgroundWidth < widgetWidth) {
xOffset += (widgetWidth - backgroundWidth) / 2 - margin;
}
const barHeight = 8;
let widgetYBar = widgetY + backgroundHeight + margin;
// Draw progress bar border
ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR;
ctx.fillRect(
widgetX - border,
widgetYBar - border,
backgroundWidth + border * 2,
barHeight + border * 2
);
// Draw progress bar area
ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR; // Mismo color de fondo que el canvas
ctx.fillRect(
widgetX,
widgetYBar,
backgroundWidth,
barHeight
);
// Draw progress bar grid
ctx.beginPath();
ctx.lineWidth = 1;
ctx.strokeStyle = "#66666650";
// Determine max lines
const numLines = Math.floor(backgroundWidth / 64);
// Draw progress bar grid
for (let x = 0; x <= width / 64; x += 1) {
ctx.moveTo(widgetX + x * 64 * scale, widgetYBar);
ctx.lineTo(widgetX + x * 64 * scale, widgetYBar + barHeight);
}
ctx.stroke();
ctx.closePath();
// Draw progress bar
const progress = Math.min(blurRadius / 255, 1);
ctx.fillStyle = "rgba(0, 120, 255, 0.5)";
ctx.fillRect(
widgetX,
widgetYBar,
backgroundWidth * progress,
barHeight
);
}
};
widget.canvas = document.createElement("canvas");
widget.canvas.className = "mask-rect-area-canvas";
widget.parent = node;
document.body.appendChild(widget.canvas);
node.addCustomWidget(widget);
app.canvas.onDrawBackground = function () {
// Draw node isnt fired once the node is off the screen
// if it goes off screen quickly, the input may not be removed
// this shifts it off screen so it can be moved back if the node is visible.
for (let n in app.graph._nodes) {
n = app.graph._nodes[n];
for (let w in n.widgets) {
let wid = n.widgets[w];
if (Object.hasOwn(wid, "canvas")) {
wid.canvas.style.left = -8000 + "px";
wid.canvas.style.position = "absolute";
}
}
}
};
node.onResize = function (size) {
computeCanvasSize(node, size);
};
return {minWidth: 200, minHeight: 200, widget};
}
app.registerExtension({
name: 'drltdata.MaskRectArea',
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (nodeData.name === "MaskRectArea") {
const onNodeCreated = nodeType.prototype.onNodeCreated;
nodeType.prototype.onNodeCreated = function () {
const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined;
this.setProperty("width", 512);
this.setProperty("height", 512);
this.setProperty("x", 0);
this.setProperty("y", 0);
this.setProperty("w", 50);
this.setProperty("h", 50);
this.setProperty("blur_radius", 0);
this.selected = false;
this.index = 3;
this.serialize_widgets = true;
CUSTOM_INT(this, "x", 0, function (v, _, node) {
this.value = Math.max(0, Math.min(100, Math.round(v))); // Limitar entre 0 y 100
node.properties["x"] = this.value;
});
CUSTOM_INT(this, "y", 0, function (v, _, node) {
this.value = Math.max(0, Math.min(100, Math.round(v)));
node.properties["y"] = this.value;
});
CUSTOM_INT(this, "w", 50, function (v, _, node) {
this.value = Math.max(0, Math.min(100, Math.round(v)));
node.properties["w"] = this.value;
});
CUSTOM_INT(this, "h", 50, function (v, _, node) {
this.value = Math.max(0, Math.min(100, Math.round(v)));
node.properties["h"] = this.value;
});
CUSTOM_INT(this, "blur_radius", 0, function (v, _, node) {
this.value = Math.round(v) || 0;
node.properties["blur_radius"] = this.value;
},
{"min": 0, "max": 255, "step": 10}
);
showPreviewCanvas(this, app);
this.onSelected = function () {
this.selected = true;
};
this.onDeselected = function () {
this.selected = false;
};
return r;
};
}
}
});
// Calculate the drawing area using percentage-based properties.
function getDrawArea(node, backgroundWidth, backgroundHeight) {
// Convert percentages to actual pixel values based on the background dimensions
let x = (node.properties["x"] / 100) * backgroundWidth;
let y = (node.properties["y"] / 100) * backgroundHeight;
let w = (node.properties["w"] / 100) * backgroundWidth;
let h = (node.properties["h"] / 100) * backgroundHeight;
// Ensure the values do not exceed the background boundaries
if (x > backgroundWidth) {
x = backgroundWidth;
}
if (y > backgroundHeight) {
y = backgroundHeight;
}
// Adjust width and height to fit within the background dimensions
if (x + w > backgroundWidth) {
w = Math.max(0, backgroundWidth - x);
}
if (y + h > backgroundHeight) {
h = Math.max(0, backgroundHeight - y);
}
return [x, y, w, h];
}
function CUSTOM_INT(node, inputName, val, func, config = {}) {
return {
widget: node.addWidget(
"number",
inputName,
val,
func,
Object.assign({}, {min: 0, max: 100, step: 10, precision: 0}, config)
)
};
}
function getDrawColor(percent, alpha) {
let h = 360 * percent;
let s = 50;
let l = 50;
l /= 100;
const a = s * Math.min(l, 1 - l) / 100;
const f = n => {
const k = (n + h / 30) % 12;
const color = l - a * Math.max(Math.min(k - 3, 9 - k, 1), -1);
return Math.round(255 * color).toString(16).padStart(2, '0'); // convert to Hex and prefix "0" if needed
};
return `#${f(0)}${f(8)}${f(4)}${alpha}`;
}
function computeCanvasSize(node, size) {
if (node.widgets[0].last_y == null) {
return;
}
const MIN_HEIGHT = 200;
const MIN_WIDTH = 200;
let y = LiteGraph.NODE_WIDGET_HEIGHT * Math.max(node.inputs.length, node.outputs.length) + 5;
let freeSpace = size[1] - y;
// Compute the height of all non-customCanvas widgets
let widgetHeight = 0;
for (let i = 0; i < node.widgets.length; i++) {
const w = node.widgets[i];
if (w.type !== "customCanvas") {
if (w.computeSize) {
widgetHeight += w.computeSize()[1] + 4;
} else {
widgetHeight += LiteGraph.NODE_WIDGET_HEIGHT + 5;
}
}
}
// Ensure there is enough vertical space
freeSpace -= widgetHeight;
// Adjust the height of the node if needed
if (freeSpace < MIN_HEIGHT) {
freeSpace = MIN_HEIGHT;
node.size[1] = y + widgetHeight + freeSpace;
node.graph.setDirtyCanvas(true);
}
// Ensure the node width meets the minimum width requirement
if (node.size[0] < MIN_WIDTH) {
node.size[0] = MIN_WIDTH;
node.graph.setDirtyCanvas(true);
}
// Position each of the widgets
for (const w of node.widgets) {
w.y = y;
if (w.type === "customCanvas") {
y += freeSpace;
} else if (w.computeSize) {
y += w.computeSize()[1] + 4;
} else {
y += LiteGraph.NODE_WIDGET_HEIGHT + 4;
}
}
node.canvasHeight = freeSpace;
}
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comfyui-impact-pack/locales/ko/nodeDefs.json
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comfyui-impact-pack/modules/impact/additional_dependencies.py
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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'])
comfyui-impact-pack/modules/impact/animatediff_nodes.py
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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.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().apply(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.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
comfyui-impact-pack/modules/impact/bridge_nodes.py
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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,
}
comfyui-impact-pack/modules/impact/config.py
-62
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@@ -1,62 +0,0 @@
import configparser
import os
import logging
version_code = [8, 22, 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'],
}
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']
if not os.path.exists(default_conf['custom_wildcards']):
logging.warning(f"[Impact Pack] custom_wildcards path not found: {default_conf['custom_wildcards']}. Using default path.")
default_conf['custom_wildcards'] = os.path.join(my_path, "..", "..", "custom_wildcards")
return {
'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
}
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
}
cached_config = None
def get_config():
global cached_config
if cached_config is None:
cached_config = read_config()
return cached_config
comfyui-impact-pack/modules/impact/core.py
-2406
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comfyui-impact-pack/modules/impact/defs.py
-17
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@@ -1,17 +0,0 @@
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"
]
comfyui-impact-pack/modules/impact/detectors.py
-529
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@@ -1,529 +0,0 @@
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):
if not isinstance(sam2_model, core.SAM2Wrapper):
logging.error("[Impact Pack] To use the SAM2VideoDetectorSEGS node, a 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`.")
segs = bbox_detector.detect(image_frames[0].unsqueeze(0), bbox_threshold, 0, 0, drop_size)
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)
comfyui-impact-pack/modules/impact/hf_nodes.py
-189
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@@ -1,189 +0,0 @@
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)
comfyui-impact-pack/modules/impact/hook_nodes.py
-128
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@@ -1,128 +0,0 @@
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,
comfyui-impact-pack/modules/impact/hooks.py
-595
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@@ -1,595 +0,0 @@
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)
comfyui-impact-pack/modules/impact/impact_onnx.py
-39
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@@ -1,39 +0,0 @@
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}")
comfyui-impact-pack/modules/impact/impact_pack.py
-2714
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comfyui-impact-pack/modules/impact/impact_sampling.py
-323
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@@ -1,323 +0,0 @@
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']().get_sigmas(float(scheduler[11:-1]), steps, denoise=1.0)[0]
elif scheduler == 'LTXV[default]':
sigmas = nodes.NODE_CLASS_MAPPINGS['LTXVScheduler']().get_sigmas(20, 2.05, 0.95, True, 0.1)[0]
elif scheduler.startswith('OSS'):
sigmas = nodes.NODE_CLASS_MAPPINGS['OptimalStepsScheduler']().get_sigmas(scheduler[4:], steps, denoise=1.0)[0]
else:
sigmas = samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, steps)
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)
comfyui-impact-pack/modules/impact/impact_server.py
-582
View File
@@ -1,582 +0,0 @@
import os
import threading
import traceback
from aiohttp import web
import impact
import folder_paths
import torchvision
import impact.core as core
import impact.impact_pack as impact_pack
from impact.utils import to_tensor
import impact.utils as utils
from segment_anything import SamPredictor, sam_model_registry
import numpy as np
import nodes
from PIL import Image
import io
import comfy
from io import BytesIO
import random
from server import PromptServer
import logging
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.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 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
if input_node['class_type'] == 'Seed (rgthree)':
input_seed = int(input_node['inputs']['seed'])
if not isinstance(input_seed, int):
continue
else:
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'})
if 'extra_data' in json_data and 'extra_pnginfo' in json_data['extra_data']:
for node in json_data['extra_data']['extra_pnginfo']['workflow']['nodes']:
key = str(node['id'])
if key in updated_widget_values:
node['widgets_values'][1] = updated_widget_values[key]
node['widgets_values'][2] = '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 as e:
logging.warning(f"[Impact Pack] ComfyUI-Impact-Pack: Error on prompt - several features will not work.\n{e}")
return json_data
PromptServer.instance.add_on_prompt_handler(onprompt)
comfyui-impact-pack/modules/impact/legacy_nodes.py
-285
View File
@@ -1,285 +0,0 @@
import folder_paths
import impact.mmdet_nodes as mmdet_nodes
from impact.utils import *
from impact.core import SEG
import impact.core as core
import nodes
class NO_BBOX_MODEL:
pass
class NO_SEGM_MODEL:
pass
class MMDetLoader:
@classmethod
def INPUT_TYPES(s):
bboxs = ["bbox/"+x for x in folder_paths.get_filename_list("mmdets_bbox")]
segms = ["segm/"+x for x in folder_paths.get_filename_list("mmdets_segm")]
return {"required": {"model_name": (bboxs + segms, )}}
RETURN_TYPES = ("BBOX_MODEL", "SEGM_MODEL")
FUNCTION = "load_mmdet"
CATEGORY = "ImpactPack/Legacy"
DEPRECATED = True
def load_mmdet(self, model_name):
mmdet_path = folder_paths.get_full_path("mmdets", model_name)
model = mmdet_nodes.load_mmdet(mmdet_path)
if model_name.startswith("bbox"):
return model, NO_SEGM_MODEL()
else:
return NO_BBOX_MODEL(), model
class BboxDetectorForEach:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bbox_model": ("BBOX_MODEL", ),
"image": ("IMAGE", ),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"dilation": ("INT", {"default": 10, "min": 0, "max": 255, "step": 1}),
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
}
}
RETURN_TYPES = ("SEGS", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Legacy"
DEPRECATED = True
@staticmethod
def detect(bbox_model, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
mmdet_results = mmdet_nodes.inference_bbox(bbox_model, image, threshold)
segmasks = core.create_segmasks(mmdet_results)
if dilation > 0:
segmasks = dilate_masks(segmasks, dilation)
items = []
h = image.shape[1]
w = image.shape[2]
for x in segmasks:
item_bbox = x[0]
item_mask = x[1]
y1, x1, y2, x2 = item_bbox
if x2 - x1 > drop_size and y2 - y1 > drop_size:
crop_region = make_crop_region(w, h, item_bbox, crop_factor)
cropped_image = crop_image(image, crop_region)
cropped_mask = crop_ndarray2(item_mask, crop_region)
confidence = x[2]
# bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h)
item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, None, None)
items.append(item)
shape = h, w
return shape, items
def doit(self, bbox_model, image, threshold, dilation, crop_factor):
return (BboxDetectorForEach.detect(bbox_model, image, threshold, dilation, crop_factor), )
class SegmDetectorCombined:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"segm_model": ("SEGM_MODEL", ),
"image": ("IMAGE", ),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"dilation": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
}
}
RETURN_TYPES = ("MASK",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Legacy"
DEPRECATED = True
def doit(self, segm_model, image, threshold, dilation):
mmdet_results = mmdet_nodes.inference_segm(image, segm_model, threshold)
segmasks = core.create_segmasks(mmdet_results)
if dilation > 0:
segmasks = dilate_masks(segmasks, dilation)
mask = combine_masks(segmasks)
return (mask,)
class BboxDetectorCombined(SegmDetectorCombined):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bbox_model": ("BBOX_MODEL", ),
"image": ("IMAGE", ),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"dilation": ("INT", {"default": 4, "min": 0, "max": 255, "step": 1}),
}
}
def doit(self, bbox_model, image, threshold, dilation):
mmdet_results = mmdet_nodes.inference_bbox(bbox_model, image, threshold)
segmasks = core.create_segmasks(mmdet_results)
if dilation > 0:
segmasks = dilate_masks(segmasks, dilation)
mask = combine_masks(segmasks)
return (mask,)
class SegmDetectorForEach:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"segm_model": ("SEGM_MODEL", ),
"image": ("IMAGE", ),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"dilation": ("INT", {"default": 10, "min": 0, "max": 255, "step": 1}),
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
}
}
RETURN_TYPES = ("SEGS", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Legacy"
DEPRECATED = True
def doit(self, segm_model, image, threshold, dilation, crop_factor):
mmdet_results = mmdet_nodes.inference_segm(image, segm_model, threshold)
segmasks = core.create_segmasks(mmdet_results)
if dilation > 0:
segmasks = dilate_masks(segmasks, dilation)
items = []
h = image.shape[1]
w = image.shape[2]
for x in segmasks:
item_bbox = x[0]
item_mask = x[1]
crop_region = make_crop_region(w, h, item_bbox, crop_factor)
cropped_image = crop_image(image, crop_region)
cropped_mask = crop_ndarray2(item_mask, crop_region)
confidence = x[2]
item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, None, None)
items.append(item)
shape = h,w
return ((shape, items), )
class SegsMaskCombine:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"segs": ("SEGS", ),
"image": ("IMAGE", ),
}
}
RETURN_TYPES = ("MASK",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Legacy"
DEPRECATED = True
@staticmethod
def combine(segs, image):
h = image.shape[1]
w = image.shape[2]
mask = np.zeros((h, w), dtype=np.uint8)
for seg in segs[1]:
cropped_mask = seg.cropped_mask
crop_region = seg.crop_region
mask[crop_region[1]:crop_region[3], crop_region[0]:crop_region[2]] |= (cropped_mask * 255).astype(np.uint8)
return torch.from_numpy(mask.astype(np.float32) / 255.0)
def doit(self, segs, image):
return (SegsMaskCombine.combine(segs, image), )
class MaskPainter(nodes.PreviewImage):
@classmethod
def INPUT_TYPES(s):
return {"required": {"images": ("IMAGE",), },
"hidden": {
"prompt": "PROMPT",
"extra_pnginfo": "EXTRA_PNGINFO",
},
"optional": {"mask_image": ("IMAGE_PATH",), },
"optional": {"image": (["#placeholder"], )},
}
RETURN_TYPES = ("MASK",)
FUNCTION = "save_painted_images"
CATEGORY = "ImpactPack/Legacy"
DEPRECATED = True
def save_painted_images(self, images, filename_prefix="impact-mask",
prompt=None, extra_pnginfo=None, mask_image=None, image=None):
if image == "#placeholder" or image['image_hash'] != id(images):
# new input image
res = self.save_images(images, filename_prefix, prompt, extra_pnginfo)
item = res['ui']['images'][0]
if not item['filename'].endswith(']'):
filepath = f"{item['filename']} [{item['type']}]"
else:
filepath = item['filename']
_, mask = nodes.LoadImage().load_image(filepath)
res['ui']['aux'] = [id(images), res['ui']['images']]
res['result'] = (mask, )
return res
else:
# new mask
if '0' in image: # fallback
image = image['0']
forward = {'filename': image['forward_filename'],
'subfolder': image['forward_subfolder'],
'type': image['forward_type'], }
res = {'ui': {'images': [forward]}}
imgpath = ""
if 'subfolder' in image and image['subfolder'] != "":
imgpath = image['subfolder'] + "/"
imgpath += f"{image['filename']}"
if 'type' in image and image['type'] != "":
imgpath += f" [{image['type']}]"
res['ui']['aux'] = [id(images), [forward]]
_, mask = nodes.LoadImage().load_image(imgpath)
res['result'] = (mask, )
return res
comfyui-impact-pack/modules/impact/logics.py
-779
View File
@@ -1,779 +0,0 @@
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)
comfyui-impact-pack/modules/impact/mmdet_nodes.py
-219
View File
@@ -1,219 +0,0 @@
import folder_paths
from impact.core import *
import os
import mmcv
from mmdet.apis import (inference_detector, init_detector)
from mmdet.evaluation import get_classes
def load_mmdet(model_path):
model_config = os.path.splitext(model_path)[0] + ".py"
model = init_detector(model_config, model_path, device="cpu")
return model
def inference_segm_old(model, image, conf_threshold):
image = image.numpy()[0] * 255
mmdet_results = inference_detector(model, image)
bbox_results, segm_results = mmdet_results
label = "A"
classes = get_classes("coco")
labels = [
np.full(bbox.shape[0], i, dtype=np.int32)
for i, bbox in enumerate(bbox_results)
]
n, m = bbox_results[0].shape
if n == 0:
return [[], [], []]
labels = np.concatenate(labels)
bboxes = np.vstack(bbox_results)
segms = mmcv.concat_list(segm_results)
filter_idxs = np.where(bboxes[:, -1] > conf_threshold)[0]
results = [[], [], []]
for i in filter_idxs:
results[0].append(label + "-" + classes[labels[i]])
results[1].append(bboxes[i])
results[2].append(segms[i])
return results
def inference_segm(image, modelname, conf_thres, lab="A"):
image = image.numpy()[0] * 255
mmdet_results = inference_detector(modelname, image).pred_instances
bboxes = mmdet_results.bboxes.numpy()
segms = mmdet_results.masks.numpy()
scores = mmdet_results.scores.numpy()
classes = get_classes("coco")
n, m = bboxes.shape
if n == 0:
return [[], [], [], []]
labels = mmdet_results.labels
filter_inds = np.where(mmdet_results.scores > conf_thres)[0]
results = [[], [], [], []]
for i in filter_inds:
results[0].append(lab + "-" + classes[labels[i]])
results[1].append(bboxes[i])
results[2].append(segms[i])
results[3].append(scores[i])
return results
def inference_bbox(modelname, image, conf_threshold):
image = image.numpy()[0] * 255
label = "A"
output = inference_detector(modelname, image).pred_instances
cv2_image = np.array(image)
cv2_image = cv2_image[:, :, ::-1].copy()
cv2_gray = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2GRAY)
segms = []
for x0, y0, x1, y1 in output.bboxes:
cv2_mask = np.zeros(cv2_gray.shape, np.uint8)
cv2.rectangle(cv2_mask, (int(x0), int(y0)), (int(x1), int(y1)), 255, -1)
cv2_mask_bool = cv2_mask.astype(bool)
segms.append(cv2_mask_bool)
n, m = output.bboxes.shape
if n == 0:
return [[], [], [], []]
bboxes = output.bboxes.numpy()
scores = output.scores.numpy()
filter_idxs = np.where(scores > conf_threshold)[0]
results = [[], [], [], []]
for i in filter_idxs:
results[0].append(label)
results[1].append(bboxes[i])
results[2].append(segms[i])
results[3].append(scores[i])
return results
class BBoxDetector:
bbox_model = None
def __init__(self, bbox_model):
self.bbox_model = bbox_model
def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
drop_size = max(drop_size, 1)
mmdet_results = inference_bbox(self.bbox_model, image, threshold)
segmasks = create_segmasks(mmdet_results)
if dilation > 0:
segmasks = dilate_masks(segmasks, dilation)
items = []
h = image.shape[1]
w = image.shape[2]
for x in segmasks:
item_bbox = x[0]
item_mask = x[1]
y1, x1, y2, x2 = item_bbox
if x2 - x1 > drop_size and y2 - y1 > drop_size: # minimum dimension must be (2,2) to avoid squeeze issue
crop_region = make_crop_region(w, h, item_bbox, crop_factor)
cropped_image = crop_image(image, crop_region)
cropped_mask = crop_ndarray2(item_mask, crop_region)
confidence = x[2]
# bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h)
item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, None, None)
items.append(item)
shape = image.shape[1], image.shape[2]
return shape, items
def detect_combined(self, image, threshold, dilation):
mmdet_results = inference_bbox(self.bbox_model, image, threshold)
segmasks = create_segmasks(mmdet_results)
if dilation > 0:
segmasks = dilate_masks(segmasks, dilation)
return combine_masks(segmasks)
def setAux(self, x):
pass
class SegmDetector(BBoxDetector):
segm_model = None
def __init__(self, segm_model):
self.segm_model = segm_model
def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
drop_size = max(drop_size, 1)
mmdet_results = inference_segm(image, self.segm_model, threshold)
segmasks = create_segmasks(mmdet_results)
if dilation > 0:
segmasks = dilate_masks(segmasks, dilation)
items = []
h = image.shape[1]
w = image.shape[2]
for x in segmasks:
item_bbox = x[0]
item_mask = x[1]
y1, x1, y2, x2 = item_bbox
if x2 - x1 > drop_size and y2 - y1 > drop_size: # minimum dimension must be (2,2) to avoid squeeze issue
crop_region = make_crop_region(w, h, item_bbox, crop_factor)
cropped_image = crop_image(image, crop_region)
cropped_mask = crop_ndarray2(item_mask, crop_region)
confidence = x[2]
item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, None, None)
items.append(item)
segs = image.shape, items
if detailer_hook is not None and hasattr(detailer_hook, "post_detection"):
segs = detailer_hook.post_detection(segs)
return segs
def detect_combined(self, image, threshold, dilation):
mmdet_results = inference_bbox(self.bbox_model, image, threshold)
segmasks = create_segmasks(mmdet_results)
if dilation > 0:
segmasks = dilate_masks(segmasks, dilation)
return combine_masks(segmasks)
def setAux(self, x):
pass
class MMDetDetectorProvider:
@classmethod
def INPUT_TYPES(s):
bboxs = ["bbox/"+x for x in folder_paths.get_filename_list("mmdets_bbox")]
segms = ["segm/"+x for x in folder_paths.get_filename_list("mmdets_segm")]
return {"required": {"model_name": (bboxs + segms, )}}
RETURN_TYPES = ("BBOX_DETECTOR", "SEGM_DETECTOR")
FUNCTION = "load_mmdet"
CATEGORY = "ImpactPack"
def load_mmdet(self, model_name):
mmdet_path = folder_paths.get_full_path("mmdets", model_name)
model = load_mmdet(mmdet_path)
if model_name.startswith("bbox"):
return BBoxDetector(model), NO_SEGM_DETECTOR()
else:
return NO_BBOX_DETECTOR(), model
comfyui-impact-pack/modules/impact/pipe.py
-440
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@@ -1,440 +0,0 @@
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",),
},
}
comfyui-impact-pack/modules/impact/segs_nodes.py
-2025
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comfyui-impact-pack/modules/impact/segs_upscaler.py
-140
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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().apply(model)[0]
if control_net_wrapper is not None:
positive, negative, _ = control_net_wrapper.apply(positive, negative, image, noise_mask)
# prepare mask
if noise_mask is not None and inpaint_model:
imc_encode = nodes.InpaintModelConditioning().encode
if 'noise_mask' in inspect.signature(imc_encode).parameters:
positive, negative, latent_image = imc_encode(positive, negative, image, vae, mask=noise_mask, noise_mask=True)
else:
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
comfyui-impact-pack/modules/impact/special_samplers.py
-686
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@@ -1,686 +0,0 @@
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.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.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.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.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", )
comfyui-impact-pack/modules/impact/util_nodes.py
-738
View File
@@ -1,738 +0,0 @@
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) == 1:
mask = make_3d_mask(mask[0])
return (mask,)
elif len(mask) > 1:
mask1 = make_3d_mask(mask[0])
for mask2 in mask[1:]:
mask2 = make_3d_mask(mask2)
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,)
else:
empty_mask = torch.zeros((1, 64, 64), dtype=torch.float32, device="cpu").unsqueeze(0)
return (empty_mask,)
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) <= 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 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": 0, "max": sys.maxsize, "step": 1, "tooltip": "The index of the item you want to select from the list."}),
}
}
RETURN_TYPES = (any_typ,)
INPUT_IS_LIST = True
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
DESCRIPTION = "Selects the Nth item from a list. If the index is out of range, it returns the last item in the list."
def doit(self, any_list, index):
i = index[0]
if i >= len(any_list):
return (any_list[-1],)
else:
return (any_list[i],)
class MakeImageList:
@classmethod
def INPUT_TYPES(s):
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)
comfyui-impact-pack/modules/impact/utils.py
-743
View File
@@ -1,743 +0,0 @@
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("*")
comfyui-impact-pack/modules/impact/wildcards.py
-624
View File
@@ -1,624 +0,0 @@
import re
import random
import os
import nodes
import folder_paths
import yaml
import numpy as np
import threading
from impact import utils
from impact import config
import logging
wildcards_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "wildcards"))
RE_WildCardQuantifier = re.compile(r"(?P<quantifier>\d+)#__(?P<keyword>[\w.\-+/*\\]+?)__", re.IGNORECASE)
wildcard_lock = threading.Lock()
wildcard_dict = {}
def get_wildcard_list():
with wildcard_lock:
return [f"__{x}__" for x in wildcard_dict.keys()]
def get_wildcard_dict():
global wildcard_dict
with wildcard_lock:
return wildcard_dict
def wildcard_normalize(x):
return x.replace("\\", "/").replace(' ', '-').lower()
def read_wildcard(k, v):
if isinstance(v, list):
k = wildcard_normalize(k)
wildcard_dict[k] = v
elif isinstance(v, dict):
for k2, v2 in v.items():
new_key = f"{k}/{k2}"
new_key = wildcard_normalize(new_key)
read_wildcard(new_key, v2)
elif isinstance(v, str):
k = wildcard_normalize(k)
wildcard_dict[k] = [v]
elif isinstance(v, (int, float)):
k = wildcard_normalize(k)
wildcard_dict[k] = [str(v)]
def read_wildcard_dict(wildcard_path):
global wildcard_dict
for root, directories, files in os.walk(wildcard_path, followlinks=True):
for file in files:
if file.endswith('.txt'):
file_path = os.path.join(root, file)
rel_path = os.path.relpath(file_path, wildcard_path)
key = wildcard_normalize(os.path.splitext(rel_path)[0])
try:
with open(file_path, 'r', encoding="ISO-8859-1") as f:
lines = f.read().splitlines()
wildcard_dict[key] = [x for x in lines if not x.strip().startswith('#')]
except yaml.reader.ReaderError:
with open(file_path, 'r', encoding="UTF-8", errors="ignore") as f:
lines = f.read().splitlines()
wildcard_dict[key] = [x for x in lines if not x.strip().startswith('#')]
elif file.endswith('.yaml') or file.endswith('.yml'):
file_path = os.path.join(root, file)
try:
with open(file_path, 'r', encoding="ISO-8859-1") as f:
yaml_data = yaml.load(f, Loader=yaml.FullLoader)
except yaml.reader.ReaderError:
with open(file_path, 'r', encoding="UTF-8", errors="ignore") as f:
yaml_data = yaml.load(f, Loader=yaml.FullLoader)
for k, v in yaml_data.items():
read_wildcard(k, v)
return wildcard_dict
def process_comment_out(text):
lines = text.split('\n')
lines0 = []
flag = False
for line in lines:
if line.lstrip().startswith('#'):
flag = True
continue
if len(lines0) == 0:
lines0.append(line)
elif flag:
lines0[-1] += ' ' + line
flag = False
else:
lines0.append(line)
return '\n'.join(lines0)
def process(text, seed=None):
text = process_comment_out(text)
if seed is not None:
random.seed(seed)
random_gen = np.random.default_rng(seed)
local_wildcard_dict = get_wildcard_dict()
def replace_options(string):
replacements_found = False
def replace_option(match):
nonlocal replacements_found
options = match.group(1).split('|')
multi_select_pattern = options[0].split('$$')
select_range = None
select_sep = ' '
range_pattern = r'(\d+)(-(\d+))?'
range_pattern2 = r'-(\d+)'
wildcard_pattern = r"__([\w.\-+/*\\]+?)__"
if len(multi_select_pattern) > 1:
r = re.match(range_pattern, options[0])
if r is None:
r = re.match(range_pattern2, options[0])
a = '1'
b = r.group(1).strip()
else:
a = r.group(1).strip()
b = r.group(3)
if b is not None:
b = b.strip()
else:
b = a
if r is not None:
if b is not None and is_numeric_string(a) and is_numeric_string(b):
# PATTERN: num1-num2
select_range = int(a), int(b)
elif is_numeric_string(a):
# PATTERN: num
x = int(a)
select_range = (x, x)
# Expand wildcard path or return the string after $$
def expand_wildcard_or_return_string(options, pattern, wildcard_pattern):
matches = re.findall(wildcard_pattern, pattern)
if len(options) == 1 and matches:
# $$<single wildcard>
return get_wildcard_options(pattern)
else:
# $$opt1|opt2|...
options[0] = pattern
return options
if select_range is not None and len(multi_select_pattern) == 2:
# PATTERN: count$$
options = expand_wildcard_or_return_string(options, multi_select_pattern[1], wildcard_pattern )
elif select_range is not None and len(multi_select_pattern) == 3:
# PATTERN: count$$ sep $$
select_sep = multi_select_pattern[1]
options = expand_wildcard_or_return_string(options, multi_select_pattern[2], wildcard_pattern )
adjusted_probabilities = []
total_prob = 0
for option in options:
parts = option.split('::', 1) if isinstance(option, str) else f"{option}".split('::', 1)
if len(parts) == 2 and is_numeric_string(parts[0].strip()):
config_value = float(parts[0].strip())
else:
config_value = 1 # Default value if no configuration is provided
adjusted_probabilities.append(config_value)
total_prob += config_value
normalized_probabilities = [prob / total_prob for prob in adjusted_probabilities]
if select_range is None:
select_count = 1
else:
def calculate_max(_options_length, _max_select_range):
return min(_max_select_range + 1, _options_length + 1) if _max_select_range > 0 else _options_length + 1
def calculate_select_count(_max_value, _min_select_range, random_gen):
if max(_max_value, _min_select_range) <= 0:
return 0
# fix: low >= high
elif _max_value == _min_select_range:
return _max_value
else:
# fix: low >= high
_low_value = min(_min_select_range, _max_value)
_high_value = max(_min_select_range, _max_value)
return random_gen.integers(low=_low_value, high=_high_value, size=1)
select_count = calculate_select_count(calculate_max(len(options), select_range[1]), select_range[0], random_gen)
if select_count > len(options) or total_prob <= 1:
random_gen.shuffle(options)
selected_items = options
else:
selected_items = random_gen.choice(options, p=normalized_probabilities, size=select_count, replace=False)
# x may be numpy.int32, convert to string
selected_items2 = [re.sub(r'^\s*[0-9.]+::', '', str(x), count=1) for x in selected_items]
replacement = select_sep.join(selected_items2)
if '::' in replacement:
pass
replacements_found = True
return replacement
pattern = r'(?<!\\)\{((?:[^{}]|(?<=\\)[{}])*?)(?<!\\)\}'
replaced_string = re.sub(pattern, replace_option, string)
return replaced_string, replacements_found
def get_wildcard_options(string):
pattern = r"__([\w.\-+/*\\]+?)__"
matches = re.findall(pattern, string)
options = []
for match in matches:
keyword = match.lower()
keyword = wildcard_normalize(keyword)
if keyword in local_wildcard_dict:
options.extend(local_wildcard_dict[keyword])
elif '*' in keyword:
subpattern = keyword.replace('*', '.*').replace('+', '\\+')
total_patterns = []
found = False
for k, v in local_wildcard_dict.items():
if re.match(subpattern, k) is not None or re.match(subpattern, k+'/') is not None:
total_patterns += v
found = True
if found:
options.extend(total_patterns)
elif '/' not in keyword:
string_fallback = string.replace(f"__{match}__", f"__*/{match}__", 1)
options.extend(get_wildcard_options(string_fallback))
return options
def replace_wildcard(string):
pattern = r"__([\w.\-+/*\\]+?)__"
matches = re.findall(pattern, string)
replacements_found = False
for match in matches:
keyword = match.lower()
keyword = wildcard_normalize(keyword)
if keyword in local_wildcard_dict:
# look for adjusted probability
adjusted_probabilities = []
total_prob = 0
options=local_wildcard_dict[keyword]
for option in options:
parts = option.split('::', 1)
if len(parts) == 2 and is_numeric_string(parts[0].strip()):
config_value = float(parts[0].strip())
else:
config_value = 1 # Default value if no configuration is provided
adjusted_probabilities.append(config_value)
total_prob += config_value
normalized_probabilities = [prob / total_prob for prob in adjusted_probabilities]
selected_item = random_gen.choice(options, p=normalized_probabilities, replace=False)
replacement = re.sub(r'^\s*[0-9.]+::', '', selected_item, count=1)
replacements_found = True
string = string.replace(f"__{match}__", replacement, 1)
elif '*' in keyword:
subpattern = keyword.replace('*', '.*').replace('+', '\\+')
total_patterns = []
found = False
for k, v in local_wildcard_dict.items():
if re.match(subpattern, k) is not None or re.match(subpattern, k+'/') is not None:
total_patterns += v
found = True
if found:
replacement = random_gen.choice(total_patterns)
replacements_found = True
string = string.replace(f"__{match}__", replacement, 1)
elif '/' not in keyword:
string_fallback = string.replace(f"__{match}__", f"__*/{match}__", 1)
string, replacements_found = replace_wildcard(string_fallback)
return string, replacements_found
replace_depth = 100
stop_unwrap = False
while not stop_unwrap and replace_depth > 1:
replace_depth -= 1 # prevent infinite loop
option_quantifier = [e.groupdict() for e in RE_WildCardQuantifier.finditer(text)]
for match in option_quantifier:
keyword = match['keyword'].lower()
quantifier = int(match['quantifier']) if match['quantifier'] else 1
replacement = '__|__'.join([keyword,] * quantifier)
wilder_keyword = keyword.replace('*', '\\*')
RE_TEMP = re.compile(fr"(?P<quantifier>\d+)#__(?P<keyword>{wilder_keyword})__", re.IGNORECASE)
text = RE_TEMP.sub(f"__{replacement}__", text)
# pass1: replace options
pass1, is_replaced1 = replace_options(text)
while is_replaced1:
pass1, is_replaced1 = replace_options(pass1)
# pass2: replace wildcards
text, is_replaced2 = replace_wildcard(pass1)
stop_unwrap = not is_replaced1 and not is_replaced2
return text
def is_numeric_string(input_str):
return re.match(r'^-?(\d*\.?\d+|\d+\.?\d*)$', input_str) is not None
def safe_float(x):
if is_numeric_string(x):
return float(x)
else:
return 1.0
def extract_lora_values(string):
pattern = r'<lora:([^>]+)>'
matches = re.findall(pattern, string)
def touch_lbw(text):
return re.sub(r'LBW=[A-Za-z][A-Za-z0-9_-]*:', r'LBW=', text)
items = [touch_lbw(match.strip(':')) for match in matches]
added = set()
result = []
for item in items:
item = item.split(':')
lora = None
a = None
b = None
lbw = None
lbw_a = None
lbw_b = None
loader = None
if len(item) > 0:
lora = item[0]
for sub_item in item[1:]:
if is_numeric_string(sub_item):
if a is None:
a = float(sub_item)
elif b is None:
b = float(sub_item)
elif sub_item.startswith("LBW="):
for lbw_item in sub_item[4:].split(';'):
if lbw_item.startswith("A="):
lbw_a = safe_float(lbw_item[2:].strip())
elif lbw_item.startswith("B="):
lbw_b = safe_float(lbw_item[2:].strip())
elif lbw_item.strip() != '':
lbw = lbw_item
elif sub_item.startswith("LOADER="):
loader = sub_item[7:]
if a is None:
a = 1.0
if b is None:
b = a
if lora is not None and lora not in added:
result.append((lora, a, b, lbw, lbw_a, lbw_b, loader))
added.add(lora)
return result
def remove_lora_tags(string):
pattern = r'<lora:[^>]+>'
result = re.sub(pattern, '', string)
return result
def resolve_lora_name(lora_name_cache, name):
if os.path.exists(name):
return name
else:
if len(lora_name_cache) == 0:
lora_name_cache.extend(folder_paths.get_filename_list("loras"))
for x in lora_name_cache:
if x.endswith(name):
return x
return None
def process_with_loras(wildcard_opt, model, clip, clip_encoder=None, seed=None, processed=None):
"""
process wildcard text including loras
:param wildcard_opt: wildcard text
:param model: model
:param clip: clip
:param clip_encoder: you can pass custom encoder such as adv_cliptext_encode
:param seed: seed for populating
:param processed: output variable - [pass1, pass2, pass3] will be saved into passed list
:return: model, clip, conditioning
"""
lora_name_cache = []
pass1 = process(wildcard_opt, seed)
loras = extract_lora_values(pass1)
pass2 = remove_lora_tags(pass1)
for lora_name, model_weight, clip_weight, lbw, lbw_a, lbw_b, loader in loras:
lora_name_ext = lora_name.split('.')
if ('.'+lora_name_ext[-1]) not in folder_paths.supported_pt_extensions:
lora_name = lora_name+".safetensors"
orig_lora_name = lora_name
lora_name = resolve_lora_name(lora_name_cache, lora_name)
if lora_name is not None:
path = folder_paths.get_full_path("loras", lora_name)
else:
path = None
if path is not None:
logging.info(f"LOAD LORA: {lora_name}: {model_weight}, {clip_weight}, LBW={lbw}, A={lbw_a}, B={lbw_b}, LOADER={loader}")
if loader is not None:
if loader == 'nunchaku':
if 'NunchakuFluxLoraLoader' not in nodes.NODE_CLASS_MAPPINGS:
logging.warning("To use `LOADER=nunchaku`, 'ComfyUI-nunchaku' is required. The LOADER= attribute is being ignored.")
cls = nodes.NODE_CLASS_MAPPINGS['NunchakuFluxLoraLoader']
model = cls().load_lora(model, lora_name, model_weight)[0]
else:
logging.warning(f"LORA LOADER NOT FOUND: '{loader}'")
else:
def default_lora():
return nodes.LoraLoader().load_lora(model, clip, lora_name, model_weight, clip_weight)
if lbw is not None:
if 'LoraLoaderBlockWeight //Inspire' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node(
'https://github.com/ltdrdata/ComfyUI-Inspire-Pack',
"To use 'LBW=' syntax in wildcards, 'Inspire Pack' extension is required.")
logging.warning("'LBW(Lora Block Weight)' is given, but the 'Inspire Pack' is not installed. The LBW= attribute is being ignored.")
model, clip = default_lora()
else:
cls = nodes.NODE_CLASS_MAPPINGS['LoraLoaderBlockWeight //Inspire']
model, clip, _ = cls().doit(model, clip, lora_name, model_weight, clip_weight, False, 0, lbw_a, lbw_b, "", lbw)
else:
model, clip = default_lora()
else:
logging.warning(f"LORA NOT FOUND: {orig_lora_name}")
pass3 = [x.strip() for x in pass2.split("BREAK")]
pass3 = [x for x in pass3 if x != '']
if len(pass3) == 0:
pass3 = ['']
pass3_str = [f'[{x}]' for x in pass3]
logging.info(f"CLIP: {str.join(' + ', pass3_str)}")
result = None
for prompt in pass3:
if clip_encoder is None:
cur = nodes.CLIPTextEncode().encode(clip, prompt)[0]
else:
cur = clip_encoder.encode(clip, prompt)[0]
if result is not None:
result = nodes.ConditioningConcat().concat(result, cur)[0]
else:
result = cur
if processed is not None:
processed.append(pass1)
processed.append(pass2)
processed.append(pass3)
return model, clip, result
def starts_with_regex(pattern, text):
regex = re.compile(pattern)
return regex.match(text)
def split_to_dict(text):
pattern = r'\[([A-Za-z0-9_. ]+)\]([^\[]+)(?=\[|$)'
matches = re.findall(pattern, text)
result_dict = {key: value.strip() for key, value in matches}
return result_dict
class WildcardChooser:
def __init__(self, items, randomize_when_exhaust):
self.i = 0
self.items = items
self.randomize_when_exhaust = randomize_when_exhaust
def get(self, seg):
if self.i >= len(self.items):
self.i = 0
if self.randomize_when_exhaust:
random.shuffle(self.items)
item = self.items[self.i]
self.i += 1
return item
class WildcardChooserDict:
def __init__(self, items):
self.items = items
def get(self, seg):
text = ""
if 'ALL' in self.items:
text = self.items['ALL']
if seg.label in self.items:
text += self.items[seg.label]
return text
def split_string_with_sep(input_string):
sep_pattern = r'\[SEP(?:\:\w+)?\]'
substrings = re.split(sep_pattern, input_string)
result_list = [None]
matches = re.findall(sep_pattern, input_string)
for i, substring in enumerate(substrings):
result_list.append(substring)
if i < len(matches):
if matches[i] == '[SEP]':
result_list.append(None)
elif matches[i] == '[SEP:R]':
result_list.append(random.randint(0, 1125899906842624))
else:
try:
seed = int(matches[i][5:-1])
except Exception:
seed = None
result_list.append(seed)
iterable = iter(result_list)
return list(zip(iterable, iterable))
def process_wildcard_for_segs(wildcard):
if wildcard.startswith('[LAB]'):
raw_items = split_to_dict(wildcard)
items = {}
for k, v in raw_items.items():
v = v.strip()
if v != '':
items[k] = v
return 'LAB', WildcardChooserDict(items)
else:
match = starts_with_regex(r"\[(ASC-SIZE|DSC-SIZE|ASC|DSC|RND)\]", wildcard)
if match:
mode = match[1]
items = split_string_with_sep(wildcard[len(match[0]):])
if mode == 'RND':
random.shuffle(items)
return mode, WildcardChooser(items, True)
else:
return mode, WildcardChooser(items, False)
else:
return None, WildcardChooser([(None, wildcard)], False)
def wildcard_load():
global wildcard_dict
wildcard_dict = {}
with wildcard_lock:
read_wildcard_dict(wildcards_path)
try:
read_wildcard_dict(config.get_config()['custom_wildcards'])
except Exception:
logging.info("[Impact Pack] Failed to load custom wildcards directory.")
logging.info("[Impact Pack] Wildcards loading done.")
comfyui-impact-pack/modules/thirdparty/noise_nodes.py
Vendored
-83
View File
@@ -1,83 +0,0 @@
# 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,)
comfyui-impact-pack/node_list.json
-4
View File
@@ -1,4 +0,0 @@
{
"Segs Mask": "This node is renamed to 'ImpactSegsAndMask'",
"Segs Mask ForEach": "This node is renamed to 'ImpactSegsAndMaskForEach'"
}
comfyui-impact-pack/notebook/comfyui_colab_impact_pack.ipynb
-172
View File
@@ -1,172 +0,0 @@
{
"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
}
comfyui-impact-pack/pyproject.toml
-15
View File
@@ -1,15 +0,0 @@
[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 = "8.22.2"
license = { file = "LICENSE.txt" }
dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]
[project.urls]
Repository = "https://github.com/ltdrdata/ComfyUI-Impact-Pack"
# Used by Comfy Registry https://comfyregistry.org
[tool.comfy]
PublisherId = "drltdata"
DisplayName = "ComfyUI Impact Pack"
Icon = ""
comfyui-impact-pack/requirements.txt
-10
View File
@@ -1,10 +0,0 @@
segment-anything
scikit-image
piexif
transformers
opencv-python-headless
scipy>=1.11.4
numpy
dill
matplotlib
git+https://github.com/facebookresearch/sam2
comfyui-impact-pack/ruff.toml
-3
View File
@@ -1,3 +0,0 @@
[lint]
ignore = ["E402","E701"]
exclude = ["install.py", "*.ipynb"]
comfyui-impact-pack/test/advanced-sampler.json
-976
View File
@@ -1,976 +0,0 @@
{
"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": [
{
"name": "basic_pipe",
"type": "BASIC_PIPE",
"link": 15
},
{
"name": "model",
"type": "MODEL",
"link": null
},
{
"name": "clip",
"type": "CLIP",
"link": null
},
{
"name": "vae",
"type": "VAE",
"link": null
},
{
"name": "positive",
"type": "CONDITIONING",
"link": 17
},
{
"name": "negative",
"type": "CONDITIONING",
"link": null
}
],
"outputs": [
{
"name": "basic_pipe",
"type": "BASIC_PIPE",
"links": [
20
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "EditBasicPipe"
}
},
{
"id": 12,
"type": "CLIPTextEncode",
"pos": [
420,
670
],
"size": {
"0": 422.84503173828125,
"1": 164.31304931640625
},
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "clip",
"type": "CLIP",
"link": 16
}
],
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [
17
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
"photorealistic:1.4, best quality:1.4, masterpiece, 1girl is sitting in the cafe terrace, (colorful hair:1.1)"
]
},
{
"id": 6,
"type": "CLIPTextEncode",
"pos": [
415,
186
],
"size": {
"0": 422.84503173828125,
"1": 164.31304931640625
},
"flags": {},
"order": 2,
"mode": 0,
"inputs": [
{
"name": "clip",
"type": "CLIP",
"link": 3
}
],
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [
13
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
"photorealistic:1.4, best quality:1.4, masterpiece, 1girl is sitting in the cafe terrace"
]
},
{
"id": 7,
"type": "CLIPTextEncode",
"pos": [
413,
389
],
"size": {
"0": 425.27801513671875,
"1": 180.6060791015625
},
"flags": {},
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"MASK"
]
],
"groups": [],
"config": {},
"extra": {},
"version": 0.4
}
comfyui-impact-pack/test/regional_prompt.json
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comfyui-impact-pack/troubleshooting/TROUBLESHOOTING.md
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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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comfyui-impact-pack/uninstall.py
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import os
import sys
import time
import platform
import shutil
import subprocess
comfy_path = '../..'
def rmtree(path):
retry_count = 3
while True:
try:
retry_count -= 1
if platform.system() == "Windows":
subprocess.check_call(['attrib', '-R', path + '\\*', '/S'])
shutil.rmtree(path)
return True
except Exception as ex:
print(f"ex: {ex}")
time.sleep(3)
if retry_count < 0:
raise ex
print(f"Uninstall retry({retry_count})")
js_dest_path = os.path.join(comfy_path, "web", "extensions", "impact-pack")
if os.path.exists(js_dest_path):
rmtree(js_dest_path)