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

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
jaidaken
2026-02-09 00:55:26 +00:00
parent 2b70ab9ad0
commit f09734b0ee
2274 changed files with 748556 additions and 3 deletions
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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
.idea/
resources/prompt-builder.yaml

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# ComfyUI-Inspire-Pack
This repository offers various extension nodes for ComfyUI. Nodes here have different characteristics compared to those in the ComfyUI Impact Pack. The Impact Pack has become too large now...
## Notice:
* V1.18: To use the 'OSS' Scheduler, please update to ComfyUI version 0.3.28 or later (April 13th or newer) and Impact Pack version V8.11 or higher.
* V1.9.1 To avoid confusion with the `NOISE` type in core, the type name has been changed to `NOISE_IMAGE`.
* V0.73 The Variation Seed feature is added to Regional Prompt nodes, and it is only compatible with versions Impact Pack V5.10 and above.
* V0.69 incompatible with the outdated **ComfyUI IPAdapter Plus**. (A version dated March 24th or later is required.)
* V0.64 add sigma_factor to RegionalPrompt... nodes required Impact Pack V4.76 or later.
* V0.62 support faceid in Regional IPAdapter
* V0.48 optimized wildcard node. This update requires Impact Pack V4.39.2 or later.
* V0.13.2 isn't compatible with old ControlNet Auxiliary Preprocessor. If you will use `MediaPipeFaceMeshDetectorProvider` update to latest version(Sep. 17th).
* WARN: If you use version **0.12 to 0.12.2** without a GlobalSeed node, your workflow's seed may have been erased. Please update immediately.
## Nodes
### Lora Block Weight - This is a node that provides functionality related to Lora block weight.
* This provides similar functionality to [sd-webui-lora-block-weight](https://github.com/hako-mikan/sd-webui-lora-block-weight)
* `LoRA Loader (Block Weight)`: When loading Lora, the block weight vector is applied.
* In the block vector, you can use numbers, R, A, a, B, and b.
* R is determined sequentially based on a random seed, while A and B represent the values of the A and B parameters, respectively. a and b are half of the values of A and B, respectively.
* `XY Input: LoRA Block Weight`: This is a node in the [Efficiency Nodes](https://github.com/LucianoCirino/efficiency-nodes-comfyui)' XY Plot that allows you to use Lora block weight.
* You must ensure that X and Y connections are made, and dependencies should be connected to the XY Plot.
* Note: To use this feature, update `Efficient Nodes` to a version released after September 3rd.
* Make LoRA Block Weight: Instead of directly applying the LoRA Block Weight to the MODEL, it is generated in a separate LBW_MODEL form
* Apply LoRA Block Weight: Apply LBW_MODEL to MODEL and CLIP
* Save LoRA Block Weight: Save LBW_MODEL as a .lbw.safetensors file
* Load LoRA Block Weight: Load LBW_MODEL from .lbw.safetensors file
### SEGS Supports nodes - This is a node that supports ApplyControlNet (SEGS) from the Impact Pack.
* `OpenPose Preprocessor Provider (SEGS)`: OpenPose preprocessor is applied for the purpose of using OpenPose ControlNet in SEGS.
* You need to install [ControlNet Auxiliary Preprocessors](https://github.com/Fannovel16/comfyui_controlnet_aux) to use this.
* `Canny Preprocessor Provider (SEGS)`: Canny preprocessor is applied for the purpose of using Canny ControlNet in SEGS.
* `DW Preprocessor Provider (SEGS)`, `MiDaS Depth Map Preprocessor Provider (SEGS)`, `LeReS Depth Map Preprocessor Provider (SEGS)`,
`MediaPipe FaceMesh Preprocessor Provider (SEGS)`, `HED Preprocessor Provider (SEGS)`, `Fake Scribble Preprocessor (SEGS)`,
`AnimeLineArt Preprocessor Provider (SEGS)`, `Manga2Anime LineArt Preprocessor Provider (SEGS)`, `LineArt Preprocessor Provider (SEGS)`,
`Color Preprocessor Provider (SEGS)`, `Inpaint Preprocessor Provider (SEGS)`, `Tile Preprocessor Provider (SEGS)`, `MeshGraphormer Depth Map Preprocessor Provider (SEGS)`
* `MediaPipeFaceMeshDetectorProvider`: This node provides `BBOX_DETECTOR` and `SEGM_DETECTOR` that can be used in Impact Pack's Detector using the `MediaPipe-FaceMesh Preprocessor` of ControlNet Auxiliary Preprocessors.
### A1111 Compatibility support - These nodes assists in replicating the creation of A1111 in ComfyUI exactly.
* `KSampler (Inspire)`: ComfyUI uses the CPU for generating random noise, while A1111 uses the GPU. One of the three factors that significantly impact reproducing A1111's results in ComfyUI can be addressed using `KSampler (Inspire)`.
* Other point #1 : Please make sure you haven't forgotten to include 'embedding:' in the embedding used in the prompt, like 'embedding:easynegative.'
* Other point #2 : ComfyUI and A1111 have different interpretations of weighting. To align them, you need to use [BlenderNeko/Advanced CLIP Text Encode](https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb).
* `KSamplerAdvanced (Inspire)`: Inspire Pack version of `KSampler (Advanced)`.
* `RandomNoise (inspire)`: Inspire Pack version of `RandomNoise`.
* Common Parameters
* `batch_seed_mode` determines how seeds are applied to batch latents:
* `comfy`: This method applies the noise to batch latents all at once. This is advantageous to prevent duplicate images from being generated due to seed duplication when creating images.
* `incremental`: Similar to the A1111 case, this method incrementally increases the seed and applies noise sequentially for each batch. This approach is beneficial for straightforward reproduction using only the seed.
* `variation_strength`: In each batch, the variation strength starts from the set `variation_strength` and increases by `xxx`.
* `variation_seed` and `variation_strength` - Initial noise generated by the seed is transformed to the shape of `variation_seed` by `variation_strength`. If `variation_strength` is 0, it only relies on the influence of the seed, and if `variation_strength` is 1.0, it is solely influenced by `variation_seed`.
* These parameters are used when you want to maintain the composition of an image generated by the seed but wish to introduce slight changes.
### Sampler nodes
* `KSampler Progress (Inspire)` - In KSampler, the sampling process generates latent batches. By using `Video Combine` node from [ComfyUI-VideoHelperSuite](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite), you can create a video from the progress.
* `Scheduled CFGGuider (Inspire)` - This is a CFGGuider that adjusts the schedule from from_cfg to to_cfg using linear, log, and exp methods.
* `Scheduled PerpNeg CFGGuider (Inspire)` - This is a PerpNeg CFGGuider that adjusts the schedule from from_cfg to to_cfg using linear, log, and exp methods.
### Prompt Support - These are nodes for supporting prompt processing.
* `Load Prompts From Dir (Inspire)`: It sequentially reads prompts files from the specified directory. The output it returns is ZIPPED_PROMPT.
* Specify the directories located under `ComfyUI-Inspire-Pack/prompts/`
* One prompts file can have multiple prompts separated by `---`.
* e.g. `prompts/example`
* **NOTE**: This node provides advanced option via `Show advanced`
* load_cap, start_index
* `Load Prompts From File (Inspire)`: It sequentially reads prompts from the specified file. The output it returns is ZIPPED_PROMPT.
* Specify the file located under `ComfyUI-Inspire-Pack/prompts/`
* e.g. `prompts/example/prompt2.txt`
* **NOTE**: This node provides advanced option via `Show advanced`
* load_cap, start_index
* `Load Single Prompt From File (Inspire)`: Loads a single prompt from a file containing multiple prompts by using an index.
* The prompts file directory can be specified as `inspire_prompts` in `extra_model_paths.yaml`
* `Unzip Prompt (Inspire)`: Separate ZIPPED_PROMPT into `positive`, `negative`, and name components.
* `positive` and `negative` represent text prompts, while `name` represents the name of the prompt. When loaded from a file using `Load Prompts From File (Inspire)`, the name corresponds to the file name.
* `Zip Prompt (Inspire)`: Create ZIPPED_PROMPT from positive, negative, and name_opt.
* If name_opt is omitted, it will be considered as an empty name.
* `Prompt Extractor (Inspire)`: This node reads prompt information from the image's metadata. Since it retrieves all the text, you need to directly specify the prompts to be used for `positive` and `negative` as indicated in the info.
* `Global Seed (Inspire)`: This is a node that controls the global seed without a separate connection line. It only controls when the widget's name is 'seed' or 'noise_seed'. Additionally, if 'control_before_generate' is checked, it controls the seed before executing the prompt.
* Seeds that have been converted into inputs are excluded from the target. If you want to control the seed separately, convert it into an input and control it separately.
* `Global Sampler (Inspire)`: This node is similar to GlobalSeed and can simultaneously set the sampler_name and scheduler for all nodes in the workflow.
* It applies only to nodes that have both sampler_name and scheduler, and it won't be effective if `GlobalSampler` is muted.
* If some of the `sampler_name` and `scheduler` have been converted to input and connected to Primitive node, it will not apply only to the converted widget. The widget that has not been converted to input will still be affected.
* `Bind [ImageList, PromptList] (Inspire)`: Bind Image list and zipped prompt list to export `image`, `positive`, `negative`, and `prompt_label` in a list format. If there are more prompts than images, the excess prompts are ignored, and if there are not enough, the remainder is filled with default input based on the images.
* `Wildcard Encode (Inspire)`: The combination node of [ImpactWildcardEncode](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md) and BlenderNeko's [CLIP Text Encode (Advanced)](https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb).
* To use this node, you need both the [Impact Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack) and the [Advanced CLIP Text Encode]((https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb)) extensions.
* This node is identical to `ImpactWildcardEncode`, but it encodes using `CLIP Text Encode (Advanced)` instead of the default CLIP Text Encode from ComfyUI for CLIP Text Encode.
* Requirement: Impact Pack V4.18.6 or above
* `Prompt Builder (Inspire)`: This node is a convenience node that allows you to easily assemble prompts by selecting categories and presets. To modify the presets, edit the `ComfyUI-InspirePack/resources/prompt-builder.yaml` file.
* `Seed Explorer (Inspire)`: This node helps explore seeds by allowing you to adjust the variation seed gradually in a prompt-like form.
* This feature is designed for utilizing a seed that you like, adding slight variations, and then further modifying from there when exploring.
* In the `seed_prompt`, the first seed is considered the initial seed, and the reflection rate is omitted, always defaulting to 1.0.
* Each prompt is separated by a comma, and from the second seed onwards, it should follow the format `seed:strength`.
* Pressing the "Add to prompt" button will append `additional_seed:additional_strength` to the prompt.
* `Composite Noise (Inspire)`: This node overwrites a specific area on top of the destination noise with the source noise.
* `Random Generator for List (Inspire)`: When connecting the list output to the signal input, this node generates random values for all items in the list.
* `Make Basic Pipe (Inspire)`: This is a node that creates a BASIC_PIPE using Wildcard Encode. The `Add select to` determines whether the selected item from the `Select to...` combo will be input as positive wildcard text or negative wildcard text.
* `Remove ControlNet (Inspire)`, `Remove ControlNet [RegionalPrompts] (Inspire)`: Remove ControlNet from CONDITIONING or REGIONAL_PROMPTS.
* `Remove ControlNet [RegionalPrompts] (Inspire)` requires Impact Pack V4.73.1 or above.
### Regional Nodes - These node simplifies the application of prompts by region.
* Regional Sampler - These nodes assists in the easy utilization of the regional sampler in the `Impact Pack`.
* `Regional Prompt Simple (Inspire)`: This node takes `mask` and `basic_pipe` as inputs and simplifies the creation of `REGIONAL_PROMPTS`.
* `Regional Prompt By Color Mask (Inspire)`: Similar to `Regional Prompt Simple (Inspire)`, this function accepts a color mask image as input and defines the region using the color value that will be used as the mask, instead of directly receiving the mask.
* The color value can only be in the form of a hex code like #FFFF00 or a decimal number.
* Regional Conditioning - These nodes provides assistance for simplifying the use of `Conditioning (Set Mask)`.
* `Regional Conditioning Simple (Inspire)`
* `Regional Conditioning By Color Mask (Inspire)`
* Regional IPAdapter - These nodes facilitates the convenient use of the attn_mask feature in `ComfyUI IPAdapter Plus` custom nodes.
* To use this node, you need to install the [ComfyUI IPAdapter Plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.
* `Regional IPAdapter Mask (Inspire)`, `Regional IPAdapter By Color Mask (Inspire)`
* `Regional IPAdapter Encoded Mask (Inspire)`, `Regional IPAdapter Encoded By Color Mask (Inspire)`: accept `embeds` instead of `image`
* Regional Seed Explorer - These nodes restrict the variation through a seed prompt, applying it only to the masked areas.
* `Regional Seed Explorer By Mask (Inspire)`
* `Regional Seed Explorer By Color Mask (Inspire)`
* `Regional CFG (Inspire)` - By applying a mask as a multiplier to the configured cfg, it allows different areas to have different cfg settings.
* `Color Mask To Depth Mask (Inspire)` - Convert the color map from the spec text into a mask with depth values ranging from 0.0 to 1.0.
* The range of the mask value is limited to 0.0 to 1.0.
* base_value: Sets the value of the base mask.
* dilation: Dilation applied to each mask layer before flattening.
* flatten_method: The method of flattening the mask layers.
* The layers are flattened including the base layer set by base_value.
* override: Each pixel is overwritten by the non-zero value of the upper layer.
* sum: Each pixel is flattened by summing the values of all layers.
* max: Each pixel is flattened by taking the maximum value from all layers.
### Image Util
* `Load Image Batch From Dir (Inspire)`: This is almost same as `LoadImagesFromDirectory` of [ComfyUI-Advanced-Controlnet](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet). This is just a modified version. Just note that this node forcibly normalizes the size of the loaded image to match the size of the first image, even if they are not the same size, to create a batch image.
* `Load Image List From Dir (Inspire)`: This is almost same as `Load Image Batch From Dir (Inspire)`. However, note that this node loads data in a list format, not as a batch, so it returns images at their original size without normalizing the size.
* `Load Image (Inspire)`: This node is similar to LoadImage, but the loaded image information is stored in the workflow. The image itself is stored in the workflow, making it easier to reproduce image generation on other computers.
* `Change Image Batch Size (Inspire)`: Change Image Batch Size
* `simple`: if the `batch_size` is larger than the batch size of the input image, the last frame will be duplicated. If it is smaller, it will be simply cropped.
* `Change Latent Batch Size (Inspire)`: Change Latent Batch Size
* `ImageBatchSplitter //Inspire`, `LatentBatchSplitter //Inspire`: The script divides a batch of images/latents into individual images/latents, each with a quantity equal to the specified `split_count`. An additional output slot is added for each `split_count`. If the number of images/latents exceeds the `split_count`, the remaining ones are returned as the "remained" output.
* `Color Map To Masks (Inspire)`: From the color_map, it extracts the top max_count number of colors and creates masks. min_pixels represents the minimum number of pixels for each color.
* `Select Nth Mask (Inspire)`: Extracts the nth mask from the mask batch.
### Backend Cache - Nodes for storing arbitrary data from the backend in a cache and sharing it across multiple workflows.
* `Cache Backend Data (Inspire)`: Stores any backend data in the cache using a string key. Tags are for quick reference.
* `Retrieve Backend Data (Inspire)`: Retrieves cached backend data using a string key.
* `Remove Backend Data (Inspire)`: Removes cached backend data.
* Deletion in this node only removes it from the cache managed by Inspire, and if it's still in use elsewhere, it won't be completely removed from memory.
* `signal_opt` is used to control the order of execution for this node; it will still run without a `signal_opt` input.
* When using '*' as the key, it clears all data.
* `Show Cached Info (Inspire)`: Displays information about cached data.
* Default tag cache size is 5. You can edit the default size of each tag in `cache_settings.json`.
* Runtime tag cache size can be modified on the `Show Cached Info (Inspire)` node. For example: `ckpt: 10`.
* `Cache Backend Data [NumberKey] (Inspire)`, `Retrieve Backend Data [NumberKey] (Inspire)`, `Remove Backend Data [NumberKey] (Inspire)`: These nodes are provided for convenience in the automation process, allowing the use of numbers as keys.
* `Cache Backend Data List (Inspire)`, `Cache Backend Data List [NumberKey] (Inspire)`: This node allows list input for backend cache. Conversely, nodes like `Cache Backend Data [NumberKey] (Inspire)` that do not accept list input will attempt to cache redundantly and overwrite existing data if provided with a list input. Therefore, it is necessary to use a unique key for each element to prevent this. This node caches the combined list. When retrieving cached backend data through this node, the output is in the form of a list.
* `Shared Checkpoint Loader (Inspire)`: When loading a checkpoint through this loader, it is automatically cached in the backend cache. Additionally, if it is already cached, it retrieves it from the cache instead of loading it anew.
* When `key_opt` is empty, the `ckpt_name` is set as the cache key. The cache key output can be used for deletion purposes with Remove Back End.
* This node resolves the issue of reloading checkpoints during workflow switching.
* `Shared Diffusion Model Loader (Inspire)`: Similar to the `Shared Checkpoint Loader (Inspire)` but used for loading Diffusion models instead of Checkpoints.
* `Shared Text Encoder Loader (Inspire)`: Similar to the `Shared Checkpoint Loader (Inspire)` but used for loading Text Encoder models instead of Checkpoints.
* This node also functions as a unified node for `CLIPLoader`, `DualCLIPLoader`, and `TripleCLIPLoader`.
* `Stable Cascade Checkpoint Loader (Inspire)`: This node provides a feature that allows you to load the `stage_b` and `stage_c` checkpoints of Stable Cascade at once, and it also provides a backend caching feature, optionally.
* `Is Cached (Inspire)`: Returns whether the cache exists.
### Conditioning - Nodes for conditionings
* `Concat Conditionings with Multiplier (Inspire)`: Concatenating an arbitrary number of Conditionings while applying a multiplier for each Conditioning. The multiplier depends on `comfy_PoP`, so [comfy_PoP](https://github.com/picturesonpictures/comfy_PoP) must be installed.
* `Conditioning Upscale (Inspire)`: When upscaling an image, it helps to expand the conditioning area according to the upscale factor. Taken from [ComfyUI_Dave_CustomNode](https://github.com/Davemane42/ComfyUI_Dave_CustomNode)
* `Conditioning Stretch (Inspire)`: When upscaling an image, it helps to expand the conditioning area by specifying the original resolution and the new resolution to be applied. Taken from [ComfyUI_Dave_CustomNode](https://github.com/Davemane42/ComfyUI_Dave_CustomNode)
### Models - Nodes for models
* `IPAdapter Model Helper (Inspire)`: This provides presets that allow for easy loading of the IPAdapter related models. However, it is essential for the model's name to be accurate.
* You can download the appropriate model through ComfyUI-Manager.
### List - Nodes for List processing
* `Float Range (Inspire)`: Create a float list that increases the value by `step` from `start` to `stop`. A list as large as the maximum limit is created, and when `ensure_end` is enabled, the last value of the list becomes the stop value.
* `Worklist To Item List (Inspire)`: The list in ComfyUI allows for repeated execution of a sub-workflow. This groups these repetitions (a.k.a. list) into a single ITEM_LIST output. ITEM_LIST can then be used in ForeachList.
* `▶Foreach List (Inspire)`: A starting node for performing iterative tasks by retrieving items one by one from the ITEM_LIST.\nGenerate a new intermediate_output using item and intermediate_output as inputs, then connect it to ForeachListEnd.\nNOTE:If initial_input is omitted, the first item in item_list is used as the initial value, and the processing starts from the second item in item_list.
* `Foreach List◀ (Inspire)`: An end node for performing iterative tasks by retrieving items one by one from the ITEM_LIST.\nNOTE:Directly connect the outputs of ForeachListBegin to 'flow_control' and 'remained_list'.
* `Drop List (Inspire)`: Removes all items from the ITEM_LIST. If the ITEM_LIST generated through this node is passed to ForeachListEnd, the process is immediately terminated.
### Util - Utilities
* `ToIPAdapterPipe (Inspire)`, `FromIPAdapterPipe (Inspire)`: These nodes assists in conveniently using the bundled ipadapter_model, clip_vision, and model required for applying IPAdapter.
* `List Counter (Inspire)`: When each item in the list traverses through this node, it increments a counter by one, generating an integer value.
* `RGB Hex To HSV (Inspire)`: Convert an RGB hex string like `#FFD500` to HSV:
## Credits
ComfyUI/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI.
ComfyUI/[sd-webui-lora-block-weight](https://github.com/hako-mikan/sd-webui-lora-block-weight) - The original idea for LoraBlockWeight came from here, and it is based on the syntax of this extension.
jags111/[efficiency-nodes-comfyui](https://github.com/jags111/ComfyUI-Jags-workflows) - The `XY Input` provided by the Inspire Pack supports the `XY Plot` of this node.
Fannovel16/[comfyui_controlnet_aux](https://github.com/Fannovel16/comfyui_controlnet_aux) - The wrapper for the controlnet preprocessor in the Inspire Pack depends on these nodes.
Kosinkadink/[ComfyUI-Advanced-Controlnet](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet) - `Load Images From Dir (Inspire)` code is came from here.
Trung0246/[ComfyUI-0246](https://github.com/Trung0246/ComfyUI-0246) - Nice bypass hack!
cubiq/[ComfyUI_IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) - IPAdapter related nodes depend on this extension.
Davemane42/[ComfyUI_Dave_CustomNode](https://github.com/Davemane42/ComfyUI_Dave_CustomNode) - Original author of ConditioningStretch, ConditioningUpscale
BlenderNeko/[ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) - slerp code for noise variation
BadCafeCode/[execution-inversion-demo-comfyui](https://github.com/BadCafeCode/execution-inversion-demo-comfyui) - reference loop implementation for ComfyUI

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"""
@author: Dr.Lt.Data
@title: Inspire Pack
@nickname: Inspire Pack
@description: This extension provides various nodes to support Lora Block Weight, Regional Nodes, Backend Cache, Prompt Utils, List Utils and the Impact Pack.
"""
import importlib
import logging
version_code = [1, 23]
version_str = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
logging.info(f"### Loading: ComfyUI-Inspire-Pack ({version_str})")
node_list = [
"lora_block_weight",
"segs_support",
"a1111_compat",
"prompt_support",
"inspire_server",
"image_util",
"regional_nodes",
"sampler_nodes",
"backend_support",
"list_nodes",
"conditioning_nodes",
"model_nodes",
"util_nodes"
]
NODE_CLASS_MAPPINGS = {}
NODE_DISPLAY_NAME_MAPPINGS = {}
for module_name in node_list:
imported_module = importlib.import_module(".inspire.{}".format(module_name), __name__)
NODE_CLASS_MAPPINGS = {**NODE_CLASS_MAPPINGS, **imported_module.NODE_CLASS_MAPPINGS}
NODE_DISPLAY_NAME_MAPPINGS = {**NODE_DISPLAY_NAME_MAPPINGS, **imported_module.NODE_DISPLAY_NAME_MAPPINGS}
WEB_DIRECTORY = "./js"
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]

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custom_nodes/comfyui-inspire-pack/cache_settings.json Normal file
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{
"ckpt": 5,
"latent": 100,
"image": 100
}

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custom_nodes/comfyui-inspire-pack/inspire/a1111_compat.py Normal file
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import comfy
import torch
from .libs import utils
from einops import rearrange
import random
import math
from .libs import common
import logging
supported_noise_modes = ["GPU(=A1111)", "CPU", "GPU+internal_seed", "CPU+internal_seed"]
class Inspire_RandomNoise:
def __init__(self, seed, mode, incremental_seed_mode, variation_seed, variation_strength, variation_method="linear", internal_seed=None):
device = comfy.model_management.get_torch_device()
# HOTFIX: https://github.com/comfyanonymous/ComfyUI/commit/916d1e14a93ef331adef7c0deff2fdcf443b05cf#commitcomment-151914788
# seed value should be different with generated noise
self.seed = internal_seed
self.noise_seed = seed
self.noise_device = "cpu" if mode == "CPU" else device
self.incremental_seed_mode = incremental_seed_mode
self.variation_seed = variation_seed
self.variation_strength = variation_strength
self.variation_method = variation_method
def generate_noise(self, input_latent):
latent_image = input_latent["samples"]
batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None
noise = utils.prepare_noise(latent_image, self.noise_seed, batch_inds, self.noise_device, self.incremental_seed_mode,
variation_seed=self.variation_seed, variation_strength=self.variation_strength, variation_method=self.variation_method)
return noise.cpu()
class RandomNoise:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}),
"noise_mode": (["GPU(=A1111)", "CPU"],),
"batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
"variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional":
{
"variation_method": (["linear", "slerp"],),
"internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}),
}
}
RETURN_TYPES = ("NOISE",)
FUNCTION = "get_noise"
CATEGORY = "InspirePack/a1111_compat"
def get_noise(self, noise_seed, noise_mode, batch_seed_mode, variation_seed, variation_strength, variation_method="linear", internal_seed=None):
if internal_seed is None:
internal_seed = noise_seed
return (Inspire_RandomNoise(noise_seed, noise_mode, batch_seed_mode, variation_seed, variation_strength, variation_method=variation_method, internal_seed=internal_seed),)
def inspire_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0,
noise_mode="CPU", disable_noise=False, start_step=None, last_step=None, force_full_denoise=False,
incremental_seed_mode="comfy", variation_seed=None, variation_strength=None, noise=None, callback=None, variation_method="linear",
scheduler_func=None, internal_seed=None):
device = comfy.model_management.get_torch_device()
noise_device = "cpu" if 'cpu' in noise_mode.lower() else device
latent_image = latent["samples"]
if hasattr(comfy.sample, 'fix_empty_latent_channels'):
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
latent = latent.copy()
if noise is not None and latent_image.shape[1] != noise.shape[1]:
logging.info("[Inspire Pack] inspire_ksampler: The type of latent input for noise generation does not match the model's latent type. When using the SD3 model, you must use the SD3 Empty Latent.")
raise Exception("The type of latent input for noise generation does not match the model's latent type. When using the SD3 model, you must use the SD3 Empty Latent.")
if noise is None:
if disable_noise:
torch.manual_seed(seed)
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device=noise_device)
else:
batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = utils.prepare_noise(latent_image, seed, batch_inds, noise_device, incremental_seed_mode,
variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method)
if start_step is None:
if denoise == 1.0:
start_step = 0
else:
advanced_steps = math.floor(steps / denoise)
start_step = advanced_steps - steps
steps = advanced_steps
if internal_seed is None:
internal_seed = seed
if 'internal_seed' in noise_mode:
seed = internal_seed
try:
samples = common.impact_sampling(
model=model, add_noise=not disable_noise, seed=seed, steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler, positive=positive, negative=negative,
latent_image=latent, start_at_step=start_step, end_at_step=last_step, return_with_leftover_noise=not force_full_denoise, noise=noise, callback=callback,
scheduler_func=scheduler_func)
except Exception as e:
if "unexpected keyword argument 'scheduler_func'" in str(e):
logging.info("[Inspire Pack] Impact Pack is outdated. (Cannot use GITS scheduler.)")
samples = common.impact_sampling(
model=model, add_noise=not disable_noise, seed=seed, steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler, positive=positive, negative=negative,
latent_image=latent, start_at_step=start_step, end_at_step=last_step, return_with_leftover_noise=not force_full_denoise, noise=noise, callback=callback)
else:
raise e
return samples, noise
class KSampler_inspire:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}),
"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": (common.get_schedulers(), ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"noise_mode": (supported_noise_modes,),
"batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
"variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional":
{
"variation_method": (["linear", "slerp"],),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
"internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "doit"
CATEGORY = "InspirePack/a1111_compat"
@staticmethod
def doit(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode,
batch_seed_mode="comfy", variation_seed=None, variation_strength=None, variation_method="linear", scheduler_func_opt=None,
internal_seed=None):
return (inspire_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode,
incremental_seed_mode=batch_seed_mode, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method,
scheduler_func=scheduler_func_opt, internal_seed=internal_seed)[0], )
class KSamplerAdvanced_inspire:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (common.get_schedulers(), ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
"noise_mode": (supported_noise_modes,),
"return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
"batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
"variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional":
{
"variation_method": (["linear", "slerp"],),
"noise_opt": ("NOISE_IMAGE",),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
"internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "doit"
CATEGORY = "InspirePack/a1111_compat"
@staticmethod
def sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, noise_mode, return_with_leftover_noise,
denoise=1.0, batch_seed_mode="comfy", variation_seed=None, variation_strength=None, noise_opt=None, callback=None, variation_method="linear", scheduler_func_opt=None, internal_seed=None):
force_full_denoise = True
if return_with_leftover_noise:
force_full_denoise = False
disable_noise = False
if not add_noise:
disable_noise = True
return inspire_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step,
force_full_denoise=force_full_denoise, noise_mode=noise_mode, incremental_seed_mode=batch_seed_mode,
variation_seed=variation_seed, variation_strength=variation_strength, noise=noise_opt, callback=callback, variation_method=variation_method,
scheduler_func=scheduler_func_opt, internal_seed=internal_seed)
def doit(self, *args, **kwargs):
return (self.sample(*args, **kwargs)[0],)
class KSampler_inspire_pipe:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"basic_pipe": ("BASIC_PIPE",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}),
"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": (common.get_schedulers(), ),
"latent_image": ("LATENT", ),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"noise_mode": (supported_noise_modes,),
"batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
"variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional":
{
"scheduler_func_opt": ("SCHEDULER_FUNC",),
"internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}),
}
}
RETURN_TYPES = ("LATENT", "VAE")
FUNCTION = "sample"
CATEGORY = "InspirePack/a1111_compat"
def sample(self, basic_pipe, seed, steps, cfg, sampler_name, scheduler, latent_image, denoise, noise_mode, batch_seed_mode="comfy",
variation_seed=None, variation_strength=None, scheduler_func_opt=None, internal_seed=None):
model, clip, vae, positive, negative = basic_pipe
latent = inspire_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, incremental_seed_mode=batch_seed_mode,
variation_seed=variation_seed, variation_strength=variation_strength, scheduler_func=scheduler_func_opt, internal_seed=internal_seed)[0]
return latent, vae
class KSamplerAdvanced_inspire_pipe:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"basic_pipe": ("BASIC_PIPE",),
"add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (common.get_schedulers(), ),
"latent_image": ("LATENT", ),
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
"noise_mode": (supported_noise_modes,),
"return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
"batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
"variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional":
{
"noise_opt": ("NOISE_IMAGE",),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
"internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}),
}
}
RETURN_TYPES = ("LATENT", "VAE", )
FUNCTION = "sample"
CATEGORY = "InspirePack/a1111_compat"
def sample(self, basic_pipe, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, latent_image, start_at_step, end_at_step, noise_mode, return_with_leftover_noise,
denoise=1.0, batch_seed_mode="comfy", variation_seed=None, variation_strength=None, noise_opt=None, scheduler_func_opt=None, internal_seed=None):
model, clip, vae, positive, negative = basic_pipe
latent = KSamplerAdvanced_inspire().sample(model=model, add_noise=add_noise, noise_seed=noise_seed,
steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler,
positive=positive, negative=negative, latent_image=latent_image,
start_at_step=start_at_step, end_at_step=end_at_step,
noise_mode=noise_mode, return_with_leftover_noise=return_with_leftover_noise,
denoise=denoise, batch_seed_mode=batch_seed_mode, variation_seed=variation_seed,
variation_strength=variation_strength, noise_opt=noise_opt, scheduler_func_opt=scheduler_func_opt,
internal_seed=internal_seed)[0]
return latent, vae
# Modified version of ComfyUI main code
# https://github.com/comfyanonymous/ComfyUI/blob/master/comfy_extras/nodes_hypertile.py
def get_closest_divisors(hw: int, aspect_ratio: float) -> tuple[int, int]:
pairs = [(i, hw // i) for i in range(int(math.sqrt(hw)), 1, -1) if hw % i == 0]
pair = min(((i, hw // i) for i in range(2, hw + 1) if hw % i == 0),
key=lambda x: abs(x[1] / x[0] - aspect_ratio))
pairs.append(pair)
res = min(pairs, key=lambda x: max(x) / min(x))
return res
def calc_optimal_hw(hw: int, aspect_ratio: float) -> tuple[int, int]:
hcand = round(math.sqrt(hw * aspect_ratio))
wcand = hw // hcand
if hcand * wcand != hw:
wcand = round(math.sqrt(hw / aspect_ratio))
hcand = hw // wcand
if hcand * wcand != hw:
return get_closest_divisors(hw, aspect_ratio)
return hcand, wcand
def random_divisor(value: int, min_value: int, /, max_options: int = 1, rand_obj=random.Random()) -> int:
# print(f"value={value}, min_value={min_value}, max_options={max_options}")
min_value = min(min_value, value)
# All big divisors of value (inclusive)
divisors = [i for i in range(min_value, value + 1) if value % i == 0]
ns = [value // i for i in divisors[:max_options]] # has at least 1 element
if len(ns) - 1 > 0:
idx = rand_obj.randint(0, len(ns) - 1)
else:
idx = 0
# print(f"ns={ns}, idx={idx}")
return ns[idx]
# def get_divisors(value: int, min_value: int, /, max_options: int = 1) -> list[int]:
# """
# Returns divisors of value that
# x * min_value <= value
# in big -> small order, amount of divisors is limited by max_options
# """
# max_options = max(1, max_options) # at least 1 option should be returned
# min_value = min(min_value, value)
# divisors = [i for i in range(min_value, value + 1) if value % i == 0] # divisors in small -> big order
# ns = [value // i for i in divisors[:max_options]] # has at least 1 element # big -> small order
# return ns
# def random_divisor(value: int, min_value: int, /, max_options: int = 1, rand_obj=None) -> int:
# """
# Returns a random divisor of value that
# x * min_value <= value
# if max_options is 1, the behavior is deterministic
# """
# print(f"value={value}, min_value={min_value}, max_options={max_options}")
# ns = get_divisors(value, min_value, max_options=max_options) # get cached divisors
# idx = rand_obj.randint(0, len(ns) - 1)
# print(f"ns={ns}, idx={idx}")
#
# return ns[idx]
class HyperTileInspire:
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL",),
"tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}),
"swap_size": ("INT", {"default": 2, "min": 1, "max": 128}),
"max_depth": ("INT", {"default": 0, "min": 0, "max": 10}),
"scale_depth": ("BOOLEAN", {"default": False}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "InspirePack/__for_testing"
def patch(self, model, tile_size, swap_size, max_depth, scale_depth, seed):
latent_tile_size = max(32, tile_size) // 8
temp = None
rand_obj = random.Random()
rand_obj.seed(seed)
def hypertile_in(q, k, v, extra_options):
nonlocal temp
model_chans = q.shape[-2]
orig_shape = extra_options['original_shape']
apply_to = []
for i in range(max_depth + 1):
apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i)))
if model_chans in apply_to:
shape = extra_options["original_shape"]
aspect_ratio = shape[-1] / shape[-2]
hw = q.size(1)
# h, w = calc_optimal_hw(hw, aspect_ratio)
h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1
nh = random_divisor(h, latent_tile_size * factor, swap_size, rand_obj)
nw = random_divisor(w, latent_tile_size * factor, swap_size, rand_obj)
logging.debug(f"factor: {factor} <--- params.depth: {apply_to.index(model_chans)} / scale_depth: {scale_depth} / latent_tile_size={latent_tile_size}")
# print(f"h: {h}, w:{w} --> nh: {nh}, nw: {nw}")
if nh * nw > 1:
q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
temp = (nh, nw, h, w)
# else:
# temp = None
logging.debug(f"q={q} / k={k} / v={v}")
return q, k, v
return q, k, v
def hypertile_out(out, extra_options):
nonlocal temp
if temp is not None:
nh, nw, h, w = temp
temp = None
out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
return out
m = model.clone()
m.set_model_attn1_patch(hypertile_in)
m.set_model_attn1_output_patch(hypertile_out)
return (m, )
NODE_CLASS_MAPPINGS = {
"KSampler //Inspire": KSampler_inspire,
"KSamplerAdvanced //Inspire": KSamplerAdvanced_inspire,
"KSamplerPipe //Inspire": KSampler_inspire_pipe,
"KSamplerAdvancedPipe //Inspire": KSamplerAdvanced_inspire_pipe,
"RandomNoise //Inspire": RandomNoise,
"HyperTile //Inspire": HyperTileInspire
}
NODE_DISPLAY_NAME_MAPPINGS = {
"KSampler //Inspire": "KSampler (inspire)",
"KSamplerAdvanced //Inspire": "KSamplerAdvanced (inspire)",
"KSamplerPipe //Inspire": "KSampler [pipe] (inspire)",
"KSamplerAdvancedPipe //Inspire": "KSamplerAdvanced [pipe] (inspire)",
"RandomNoise //Inspire": "RandomNoise (inspire)",
"HyperTile //Inspire": "HyperTile (Inspire)"
}

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custom_nodes/comfyui-inspire-pack/inspire/backend_support.py Normal file
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import json
import os
from .libs import common
import folder_paths
import nodes
from server import PromptServer
from .libs.utils import TaggedCache, any_typ
import logging
root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
settings_file = os.path.join(root_dir, 'cache_settings.json')
try:
with open(settings_file) as f:
cache_settings = json.load(f)
except Exception as e:
logging.error(e)
cache_settings = {}
cache = TaggedCache(cache_settings)
cache_count = {}
def update_cache(k, tag, v):
cache[k] = (tag, v)
cnt = cache_count.get(k)
if cnt is None:
cnt = 0
cache_count[k] = cnt
else:
cache_count[k] += 1
def cache_weak_hash(k):
cnt = cache_count.get(k)
if cnt is None:
cnt = 0
return k, cnt
class CacheBackendData:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"key": ("STRING", {"multiline": False, "placeholder": "Input data key (e.g. 'model a', 'chunli lora', 'girl latent 3', ...)"}),
"tag": ("STRING", {"multiline": False, "placeholder": "Tag: short description"}),
"data": (any_typ,),
}
}
RETURN_TYPES = (any_typ,)
RETURN_NAMES = ("data opt",)
FUNCTION = "doit"
CATEGORY = "InspirePack/Backend"
OUTPUT_NODE = True
@staticmethod
def doit(key, tag, data):
global cache
if key == '*':
logging.warning("[Inspire Pack] CacheBackendData: '*' is reserved key. Cannot use that key")
return (None,)
update_cache(key, tag, (False, data))
return (data,)
class CacheBackendDataNumberKey:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"key": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"tag": ("STRING", {"multiline": False, "placeholder": "Tag: short description"}),
"data": (any_typ,),
}
}
RETURN_TYPES = (any_typ,)
RETURN_NAMES = ("data opt",)
FUNCTION = "doit"
CATEGORY = "InspirePack/Backend"
OUTPUT_NODE = True
@staticmethod
def doit(key, tag, data):
global cache
update_cache(key, tag, (False, data))
return (data,)
class CacheBackendDataList:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"key": ("STRING", {"multiline": False, "placeholder": "Input data key (e.g. 'model a', 'chunli lora', 'girl latent 3', ...)"}),
"tag": ("STRING", {"multiline": False, "placeholder": "Tag: short description"}),
"data": (any_typ,),
}
}
INPUT_IS_LIST = True
RETURN_TYPES = (any_typ,)
RETURN_NAMES = ("data opt",)
OUTPUT_IS_LIST = (True,)
FUNCTION = "doit"
CATEGORY = "InspirePack/Backend"
OUTPUT_NODE = True
@staticmethod
def doit(key, tag, data):
global cache
if key == '*':
logging.warning("[Inspire Pack] CacheBackendDataList: '*' is reserved key. Cannot use that key")
return (None,)
update_cache(key[0], tag[0], (True, data))
return (data,)
class CacheBackendDataNumberKeyList:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"key": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"tag": ("STRING", {"multiline": False, "placeholder": "Tag: short description"}),
"data": (any_typ,),
}
}
INPUT_IS_LIST = True
RETURN_TYPES = (any_typ,)
RETURN_NAMES = ("data opt",)
OUTPUT_IS_LIST = (True,)
FUNCTION = "doit"
CATEGORY = "InspirePack/Backend"
OUTPUT_NODE = True
def doit(self, key, tag, data):
global cache
update_cache(key[0], tag[0], (True, data))
return (data,)
class RetrieveBackendData:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"key": ("STRING", {"multiline": False, "placeholder": "Input data key (e.g. 'model a', 'chunli lora', 'girl latent 3', ...)"}),
}
}
RETURN_TYPES = (any_typ,)
RETURN_NAMES = ("data",)
OUTPUT_IS_LIST = (True,)
FUNCTION = "doit"
CATEGORY = "InspirePack/Backend"
@staticmethod
def doit(key):
global cache
v = cache.get(key)
if v is None:
logging.warning(f"[RetrieveBackendData] '{key}' is unregistered key.")
return ([None],)
is_list, data = v[1]
if is_list:
return (data,)
else:
return ([data],)
@staticmethod
def IS_CHANGED(key):
return cache_weak_hash(key)
class RetrieveBackendDataNumberKey(RetrieveBackendData):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"key": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
}
}
class RemoveBackendData:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"key": ("STRING", {"multiline": False, "placeholder": "Input data key ('*' = clear all)"}),
},
"optional": {
"signal_opt": (any_typ,),
}
}
RETURN_TYPES = (any_typ,)
RETURN_NAMES = ("signal",)
FUNCTION = "doit"
CATEGORY = "InspirePack/Backend"
OUTPUT_NODE = True
@staticmethod
def doit(key, signal_opt=None):
global cache
if key == '*':
cache = TaggedCache(cache_settings)
elif key in cache:
del cache[key]
else:
logging.warning(f"[Inspire Pack] RemoveBackendData: invalid data key {key}")
return (signal_opt,)
class RemoveBackendDataNumberKey(RemoveBackendData):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"key": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
"optional": {
"signal_opt": (any_typ,),
}
}
@staticmethod
def doit(key, signal_opt=None):
global cache
if key in cache:
del cache[key]
else:
logging.warning(f"[Inspire Pack] RemoveBackendDataNumberKey: invalid data key {key}")
return (signal_opt,)
class ShowCachedInfo:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"cache_info": ("STRING", {"multiline": True, "default": ""}),
"key": ("STRING", {"multiline": False, "default": ""}),
},
"hidden": {"unique_id": "UNIQUE_ID"},
}
RETURN_TYPES = ()
FUNCTION = "doit"
CATEGORY = "InspirePack/Backend"
OUTPUT_NODE = True
@staticmethod
def get_data():
global cache
text1 = "---- [String Key Caches] ----\n"
text2 = "---- [Number Key Caches] ----\n"
for k, v in cache.items():
tag = 'N/A(tag)' if v[0] == '' else v[0]
if isinstance(k, str):
text1 += f'{k}: {tag}\n'
else:
text2 += f'{k}: {tag}\n'
text3 = "---- [TagCache Settings] ----\n"
for k, v in cache._tag_settings.items():
text3 += f'{k}: {v}\n'
for k, v in cache._data.items():
if k not in cache._tag_settings:
text3 += f'{k}: {v.maxsize}\n'
return f'{text1}\n{text2}\n{text3}'
@staticmethod
def set_cache_settings(data: str):
global cache
settings = data.split("---- [TagCache Settings] ----\n")[-1].strip().split("\n")
new_tag_settings = {}
for s in settings:
k, v = s.split(":")
new_tag_settings[k] = int(v.strip())
if new_tag_settings == cache._tag_settings:
# tag settings is not changed
return
new_cache = TaggedCache(new_tag_settings)
for k, v in cache.items():
new_cache[k] = v
cache = new_cache
def doit(self, cache_info, key, unique_id):
text = ShowCachedInfo.get_data()
PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": unique_id, "widget_name": "cache_info", "type": "text", "data": text})
return {}
@classmethod
def IS_CHANGED(cls, **kwargs):
return float("NaN")
class CheckpointLoaderSimpleShared(nodes.CheckpointLoaderSimple):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
"key_opt": ("STRING", {"multiline": False, "placeholder": "If empty, use 'ckpt_name' as the key."}),
},
"optional": {
"mode": (['Auto', 'Override Cache', 'Read Only'],),
}}
RETURN_TYPES = ("MODEL", "CLIP", "VAE", "STRING")
RETURN_NAMES = ("model", "clip", "vae", "cache key")
FUNCTION = "doit"
CATEGORY = "InspirePack/Backend"
def doit(self, ckpt_name, key_opt, mode='Auto'):
if mode == 'Read Only':
if key_opt.strip() == '':
raise Exception("[CheckpointLoaderSimpleShared] key_opt cannot be omit if mode is 'Read Only'")
key = key_opt.strip()
elif key_opt.strip() == '':
key = ckpt_name
else:
key = key_opt.strip()
if key not in cache or mode == 'Override Cache':
res = self.load_checkpoint(ckpt_name)
update_cache(key, "ckpt", (False, res))
cache_kind = 'ckpt'
logging.info(f"[Inspire Pack] CheckpointLoaderSimpleShared: Ckpt '{ckpt_name}' is cached to '{key}'.")
else:
cache_kind, (_, res) = cache[key]
logging.info(f"[Inspire Pack] CheckpointLoaderSimpleShared: Cached ckpt '{key}' is loaded. (Loading skip)")
if cache_kind == 'ckpt':
model, clip, vae = res
elif cache_kind == 'unclip_ckpt':
model, clip, vae, _ = res
else:
raise Exception(f"[CheckpointLoaderSimpleShared] Unexpected cache_kind '{cache_kind}'")
return model, clip, vae, key
@staticmethod
def IS_CHANGED(ckpt_name, key_opt, mode='Auto'):
if mode == 'Read Only':
if key_opt.strip() == '':
raise Exception("[CheckpointLoaderSimpleShared] key_opt cannot be omit if mode is 'Read Only'")
key = key_opt.strip()
elif key_opt.strip() == '':
key = ckpt_name
else:
key = key_opt.strip()
if mode == 'Read Only':
return (None, cache_weak_hash(key))
elif mode == 'Override Cache':
return (ckpt_name, key)
return (None, cache_weak_hash(key))
class LoadDiffusionModelShared(nodes.UNETLoader):
@classmethod
def INPUT_TYPES(s):
return {"required": { "model_name": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "Diffusion Model Name"}),
"weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],),
"key_opt": ("STRING", {"multiline": False, "placeholder": "If empty, use 'model_name' as the key."}),
"mode": (['Auto', 'Override Cache', 'Read Only'],),
}
}
RETURN_TYPES = ("MODEL", "STRING")
RETURN_NAMES = ("model", "cache key")
FUNCTION = "doit"
CATEGORY = "InspirePack/Backend"
def doit(self, model_name, weight_dtype, key_opt, mode='Auto'):
if mode == 'Read Only':
if key_opt.strip() == '':
raise Exception("[LoadDiffusionModelShared] key_opt cannot be omit if mode is 'Read Only'")
key = key_opt.strip()
elif key_opt.strip() == '':
key = f"{model_name}_{weight_dtype}"
else:
key = key_opt.strip()
if key not in cache or mode == 'Override Cache':
model = self.load_unet(model_name, weight_dtype)[0]
update_cache(key, "diffusion", (False, model))
logging.info(f"[Inspire Pack] LoadDiffusionModelShared: diffusion model '{model_name}' is cached to '{key}'.")
else:
_, (_, model) = cache[key]
logging.info(f"[Inspire Pack] LoadDiffusionModelShared: Cached diffusion model '{key}' is loaded. (Loading skip)")
return model, key
@staticmethod
def IS_CHANGED(model_name, weight_dtype, key_opt, mode='Auto'):
if mode == 'Read Only':
if key_opt.strip() == '':
raise Exception("[LoadDiffusionModelShared] key_opt cannot be omit if mode is 'Read Only'")
key = key_opt.strip()
elif key_opt.strip() == '':
key = f"{model_name}_{weight_dtype}"
else:
key = key_opt.strip()
if mode == 'Read Only':
return None, cache_weak_hash(key)
elif mode == 'Override Cache':
return model_name, key
return None, cache_weak_hash(key)
class LoadTextEncoderShared:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model_name1": (folder_paths.get_filename_list("text_encoders"), ),
"model_name2": (["None"] + folder_paths.get_filename_list("text_encoders"), ),
"model_name3": (["None"] + folder_paths.get_filename_list("text_encoders"), ),
"type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "sdxl", "flux", "hunyuan_video"], ),
"key_opt": ("STRING", {"multiline": False, "placeholder": "If empty, use 'model_name' as the key."}),
"mode": (['Auto', 'Override Cache', 'Read Only'],),
},
"optional": { "device": (["default", "cpu"], {"advanced": True}), }
}
RETURN_TYPES = ("CLIP", "STRING")
RETURN_NAMES = ("clip", "cache key")
FUNCTION = "doit"
CATEGORY = "InspirePack/Backend"
DESCRIPTION = \
("[Recipes single]\n"
"stable_diffusion: clip-l\n"
"stable_cascade: clip-g\n"
"sd3: t5 / clip-g / clip-l\n"
"stable_audio: t5\n"
"mochi: t5\n"
"cosmos: old t5 xxl\n\n"
"[Recipes dual]\n"
"sdxl: clip-l, clip-g\n"
"sd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\n"
"flux: clip-l, t5\n\n"
"[Recipes triple]\n"
"sd3: clip-l, clip-g, t5")
def doit(self, model_name1, model_name2, model_name3, type, key_opt, mode='Auto', device="default"):
if mode == 'Read Only':
if key_opt.strip() == '':
raise Exception("[LoadTextEncoderShared] key_opt cannot be omit if mode is 'Read Only'")
key = key_opt.strip()
elif key_opt.strip() == '':
key = model_name1
if model_name2 is not None:
key += f"_{model_name2}"
if model_name3 is not None:
key += f"_{model_name3}"
key += f"_{type}_{device}"
else:
key = key_opt.strip()
if key not in cache or mode == 'Override Cache':
if model_name2 != "None" and model_name3 != "None": # triple text encoder
if len({model_name1, model_name2, model_name3}) < 3:
logging.error("[LoadTextEncoderShared] The same model has been selected multiple times.")
raise ValueError("The same model has been selected multiple times.")
if type not in ["sd3"]:
logging.error("[LoadTextEncoderShared] Currently, the triple text encoder is only supported in `sd3`.")
raise ValueError("Currently, the triple text encoder is only supported in `sd3`.")
tcloader = nodes.NODE_CLASS_MAPPINGS["TripleCLIPLoader"]()
if hasattr(tcloader, 'execute'):
# node v3
res = tcloader.execute(model_name1, model_name2, model_name3)[0]
else:
# legacy compatibility
res = tcloader.load_clip(model_name1, model_name2, model_name3)[0]
elif model_name2 != "None" or model_name3 != "None": # dual text encoder
second_model = model_name2 if model_name2 != "None" else model_name3
if model_name1 == second_model:
logging.error("[LoadTextEncoderShared] You have selected the same model for both.")
raise ValueError("[LoadTextEncoderShared] You have selected the same model for both.")
if type not in ["sdxl", "sd3", "flux", "hunyuan_video"]:
logging.error("[LoadTextEncoderShared] Currently, the triple text encoder is only supported in `sdxl, sd3, flux, hunyuan_video`.")
raise ValueError("Currently, the triple text encoder is only supported in `sdxl, sd3, flux, hunyuan_video`.")
res = nodes.NODE_CLASS_MAPPINGS["DualCLIPLoader"]().load_clip(model_name1, second_model, type=type, device=device)[0]
else: # single text encoder
if type not in ["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos"]:
logging.error("[LoadTextEncoderShared] Currently, the single text encoder is only supported in `stable_diffusion, stable_cascade, sd3, stable_audio, mochi, ltxv, pixart, cosmos`.")
raise ValueError("Currently, the single text encoder is only supported in `stable_diffusion, stable_cascade, sd3, stable_audio, mochi, ltxv, pixart, cosmos`.")
res = nodes.NODE_CLASS_MAPPINGS["CLIPLoader"]().load_clip(model_name1, type=type, device=device)[0]
update_cache(key, "diffusion", (False, res))
logging.info(f"[Inspire Pack] LoadTextEncoderShared: text encoder model set is cached to '{key}'.")
else:
_, (_, res) = cache[key]
logging.info(f"[Inspire Pack] LoadTextEncoderShared: Cached text encoder model set '{key}' is loaded. (Loading skip)")
return res, key
@staticmethod
def IS_CHANGED(model_name1, model_name2, model_name3, type, key_opt, mode='Auto', device="default"):
if mode == 'Read Only':
if key_opt.strip() == '':
raise Exception("[LoadTextEncoderShared] key_opt cannot be omit if mode is 'Read Only'")
key = key_opt.strip()
elif key_opt.strip() == '':
key = model_name1
if model_name2 is not None:
key += f"_{model_name2}"
if model_name3 is not None:
key += f"_{model_name3}"
key += f"_{type}_{device}"
else:
key = key_opt.strip()
if mode == 'Read Only':
return None, cache_weak_hash(key)
elif mode == 'Override Cache':
return f"{model_name1}_{model_name2}_{model_name3}_{type}_{device}", key
return None, cache_weak_hash(key)
class StableCascade_CheckpointLoader:
@classmethod
def INPUT_TYPES(s):
ckpts = folder_paths.get_filename_list("checkpoints")
default_stage_b = ''
default_stage_c = ''
sc_ckpts = [x for x in ckpts if 'cascade' in x.lower()]
sc_b_ckpts = [x for x in sc_ckpts if 'stage_b' in x.lower()]
sc_c_ckpts = [x for x in sc_ckpts if 'stage_c' in x.lower()]
if len(sc_b_ckpts) == 0:
sc_b_ckpts = [x for x in ckpts if 'stage_b' in x.lower()]
if len(sc_c_ckpts) == 0:
sc_c_ckpts = [x for x in ckpts if 'stage_c' in x.lower()]
if len(sc_b_ckpts) == 0:
sc_b_ckpts = ckpts
if len(sc_c_ckpts) == 0:
sc_c_ckpts = ckpts
if len(sc_b_ckpts) > 0:
default_stage_b = sc_b_ckpts[0]
if len(sc_c_ckpts) > 0:
default_stage_c = sc_c_ckpts[0]
return {"required": {
"stage_b": (ckpts, {'default': default_stage_b}),
"key_opt_b": ("STRING", {"multiline": False, "placeholder": "If empty, use 'stage_b' as the key."}),
"stage_c": (ckpts, {'default': default_stage_c}),
"key_opt_c": ("STRING", {"multiline": False, "placeholder": "If empty, use 'stage_c' as the key."}),
"cache_mode": (["none", "stage_b", "stage_c", "all"], {"default": "none"}),
}}
RETURN_TYPES = ("MODEL", "VAE", "MODEL", "VAE", "CLIP_VISION", "CLIP", "STRING", "STRING")
RETURN_NAMES = ("b_model", "b_vae", "c_model", "c_vae", "c_clip_vision", "clip", "key_b", "key_c")
FUNCTION = "doit"
CATEGORY = "InspirePack/Backend"
def doit(self, stage_b, key_opt_b, stage_c, key_opt_c, cache_mode):
if key_opt_b.strip() == '':
key_b = stage_b
else:
key_b = key_opt_b.strip()
if key_opt_c.strip() == '':
key_c = stage_c
else:
key_c = key_opt_c.strip()
if cache_mode in ['stage_b', "all"]:
if key_b not in cache:
res_b = nodes.CheckpointLoaderSimple().load_checkpoint(ckpt_name=stage_b)
update_cache(key_b, "ckpt", (False, res_b))
logging.info(f"[Inspire Pack] StableCascade_CheckpointLoader: Ckpt '{stage_b}' is cached to '{key_b}'.")
else:
_, (_, res_b) = cache[key_b]
logging.info(f"[Inspire Pack] StableCascade_CheckpointLoader: Cached ckpt '{key_b}' is loaded. (Loading skip)")
b_model, clip, b_vae = res_b
else:
b_model, clip, b_vae = nodes.CheckpointLoaderSimple().load_checkpoint(ckpt_name=stage_b)
if cache_mode in ['stage_c', "all"]:
if key_c not in cache:
res_c = nodes.unCLIPCheckpointLoader().load_checkpoint(ckpt_name=stage_c)
update_cache(key_c, "unclip_ckpt", (False, res_c))
logging.info(f"[Inspire Pack] StableCascade_CheckpointLoader: Ckpt '{stage_c}' is cached to '{key_c}'.")
else:
_, (_, res_c) = cache[key_c]
logging.info(f"[Inspire Pack] StableCascade_CheckpointLoader: Cached ckpt '{key_c}' is loaded. (Loading skip)")
c_model, _, c_vae, clip_vision = res_c
else:
c_model, _, c_vae, clip_vision = nodes.unCLIPCheckpointLoader().load_checkpoint(ckpt_name=stage_c)
return b_model, b_vae, c_model, c_vae, clip_vision, clip, key_b, key_c
class IsCached:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"key": ("STRING", {"multiline": False}),
},
"hidden": {
"unique_id": "UNIQUE_ID"
}
}
RETURN_TYPES = ("BOOLEAN", )
FUNCTION = "doit"
CATEGORY = "InspirePack/Backend"
@staticmethod
def IS_CHANGED(key, unique_id):
return common.is_changed(unique_id, key in cache)
def doit(self, key, unique_id):
return (key in cache,)
# WIP: not properly working, yet
class CacheBridge:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"value": (any_typ,),
"mode": ("BOOLEAN", {"default": True, "label_off": "cached", "label_on": "passthrough"}),
},
"hidden": {
"unique_id": "UNIQUE_ID"
}
}
RETURN_TYPES = (any_typ, )
RETURN_NAMES = ("value",)
FUNCTION = "doit"
CATEGORY = "InspirePack/Backend"
@staticmethod
def IS_CHANGED(value, mode, unique_id):
if not mode and unique_id in common.changed_cache:
return common.not_changed_value(unique_id)
else:
return common.changed_value(unique_id)
def doit(self, value, mode, unique_id):
if not mode:
# cache mode
if unique_id not in common.changed_cache:
common.changed_cache[unique_id] = value
common.changed_count_cache[unique_id] = 0
return (common.changed_cache[unique_id],)
else:
common.changed_cache[unique_id] = value
common.changed_count_cache[unique_id] = 0
return (common.changed_cache[unique_id],)
NODE_CLASS_MAPPINGS = {
"CacheBackendData //Inspire": CacheBackendData,
"CacheBackendDataNumberKey //Inspire": CacheBackendDataNumberKey,
"CacheBackendDataList //Inspire": CacheBackendDataList,
"CacheBackendDataNumberKeyList //Inspire": CacheBackendDataNumberKeyList,
"RetrieveBackendData //Inspire": RetrieveBackendData,
"RetrieveBackendDataNumberKey //Inspire": RetrieveBackendDataNumberKey,
"RemoveBackendData //Inspire": RemoveBackendData,
"RemoveBackendDataNumberKey //Inspire": RemoveBackendDataNumberKey,
"ShowCachedInfo //Inspire": ShowCachedInfo,
"CheckpointLoaderSimpleShared //Inspire": CheckpointLoaderSimpleShared,
"LoadDiffusionModelShared //Inspire": LoadDiffusionModelShared,
"LoadTextEncoderShared //Inspire": LoadTextEncoderShared,
"StableCascade_CheckpointLoader //Inspire": StableCascade_CheckpointLoader,
"IsCached //Inspire": IsCached,
# "CacheBridge //Inspire": CacheBridge,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"CacheBackendData //Inspire": "Cache Backend Data (Inspire)",
"CacheBackendDataNumberKey //Inspire": "Cache Backend Data [NumberKey] (Inspire)",
"CacheBackendDataList //Inspire": "Cache Backend Data List (Inspire)",
"CacheBackendDataNumberKeyList //Inspire": "Cache Backend Data List [NumberKey] (Inspire)",
"RetrieveBackendData //Inspire": "Retrieve Backend Data (Inspire)",
"RetrieveBackendDataNumberKey //Inspire": "Retrieve Backend Data [NumberKey] (Inspire)",
"RemoveBackendData //Inspire": "Remove Backend Data (Inspire)",
"RemoveBackendDataNumberKey //Inspire": "Remove Backend Data [NumberKey] (Inspire)",
"ShowCachedInfo //Inspire": "Show Cached Info (Inspire)",
"CheckpointLoaderSimpleShared //Inspire": "Shared Checkpoint Loader (Inspire)",
"LoadDiffusionModelShared //Inspire": "Shared Diffusion Model Loader (Inspire)",
"LoadTextEncoderShared //Inspire": "Shared Text Encoder Loader (Inspire)",
"StableCascade_CheckpointLoader //Inspire": "Stable Cascade Checkpoint Loader (Inspire)",
"IsCached //Inspire": "Is Cached (Inspire)",
# "CacheBridge //Inspire": "Cache Bridge (Inspire)"
}

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custom_nodes/comfyui-inspire-pack/inspire/conditioning_nodes.py Normal file
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import torch
import nodes
import inspect
from .libs import utils
from nodes import MAX_RESOLUTION
import logging
class ConcatConditioningsWithMultiplier:
@classmethod
def INPUT_TYPES(s):
stack = inspect.stack()
if stack[1].function == 'get_input_info':
# bypass validation
class AllContainer:
def __contains__(self, item):
return True
def __getitem__(self, key):
return "FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}
return {
"required": {"conditioning1": ("CONDITIONING",), },
"optional": AllContainer()
}
return {
"required": {"conditioning1": ("CONDITIONING",), },
"optional": {"multiplier1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), },
}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "doit"
CATEGORY = "InspirePack/__for_testing"
def doit(self, **kwargs):
if "ConditioningMultiplier_PoP" in nodes.NODE_CLASS_MAPPINGS:
obj = nodes.NODE_CLASS_MAPPINGS["ConditioningMultiplier_PoP"]()
else:
utils.try_install_custom_node('https://github.com/picturesonpictures/comfy_PoP',
"To use 'ConcatConditioningsWithMultiplier' node, 'comfy_PoP' extension is required.")
raise Exception("'comfy_PoP' node isn't installed.")
conditioning_to = kwargs['conditioning1']
conditioning_to = obj.multiply_conditioning_strength(conditioning=conditioning_to, multiplier=float(kwargs['multiplier1']))[0]
out = None
for k, conditioning_from in kwargs.items():
if k == 'conditioning1' or not k.startswith('conditioning'):
continue
out = []
if len(conditioning_from) > 1:
logging.warning(f"[Inspire Pack] ConcatConditioningsWithMultiplier {k} contains more than 1 cond, only the first one will actually be applied to conditioning1.")
mkey = 'multiplier' + k[12:]
multiplier = float(kwargs[mkey])
conditioning_from = obj.multiply_conditioning_strength(conditioning=conditioning_from, multiplier=multiplier)[0]
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
if out is None:
return (kwargs['conditioning1'],)
else:
return (out,)
# CREDIT for ConditioningStretch, ConditioningUpscale: Davemane42
# Imported to support archived custom nodes.
# original code: https://github.com/Davemane42/ComfyUI_Dave_CustomNode/blob/main/MultiAreaConditioning.py
class ConditioningStretch:
def __init__(self) -> None:
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"conditioning": ("CONDITIONING",),
"resolutionX": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"resolutionY": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"newWidth": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"newHeight": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
# "scalar": ("INT", {"default": 2, "min": 1, "max": 100, "step": 0.5}),
},
}
RETURN_TYPES = ("CONDITIONING",)
CATEGORY = "InspirePack/conditioning"
FUNCTION = 'upscale'
@staticmethod
def upscale(conditioning, resolutionX, resolutionY, newWidth, newHeight, scalar=1):
c = []
for t in conditioning:
n = [t[0], t[1].copy()]
if 'area' in n[1]:
newWidth *= scalar
newHeight *= scalar
x = ((n[1]['area'][3] * 8) * newWidth / resolutionX) // 8
y = ((n[1]['area'][2] * 8) * newHeight / resolutionY) // 8
w = ((n[1]['area'][1] * 8) * newWidth / resolutionX) // 8
h = ((n[1]['area'][0] * 8) * newHeight / resolutionY) // 8
n[1]['area'] = tuple(map(lambda x: (((int(x) + 7) >> 3) << 3), [h, w, y, x]))
c.append(n)
return (c,)
class ConditioningUpscale:
def __init__(self) -> None:
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"conditioning": ("CONDITIONING",),
"scalar": ("INT", {"default": 2, "min": 1, "max": 100, "step": 0.5}),
},
}
RETURN_TYPES = ("CONDITIONING",)
CATEGORY = "InspirePack/conditioning"
FUNCTION = 'upscale'
@staticmethod
def upscale(conditioning, scalar):
c = []
for t in conditioning:
n = [t[0], t[1].copy()]
if 'area' in n[1]:
n[1]['area'] = tuple(map(lambda x: ((x * scalar + 7) >> 3) << 3, n[1]['area']))
c.append(n)
return (c,)
NODE_CLASS_MAPPINGS = {
"ConcatConditioningsWithMultiplier //Inspire": ConcatConditioningsWithMultiplier,
"ConditioningUpscale //Inspire": ConditioningUpscale,
"ConditioningStretch //Inspire": ConditioningStretch,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ConcatConditioningsWithMultiplier //Inspire": "Concat Conditionings with Multiplier (Inspire)",
"ConditioningUpscale //Inspire": "Conditioning Upscale (Inspire)",
"ConditioningStretch //Inspire": "Conditioning Stretch (Inspire)",
}

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import os
import torch
from PIL import ImageOps
try:
import pillow_jxl # noqa: F401
jxl = True
except ImportError:
jxl = False
import comfy
import folder_paths
import base64
from io import BytesIO
from .libs.utils import ByPassTypeTuple, empty_pil_tensor, empty_latent
from PIL import Image
import numpy as np
import logging
import re
def extract_first_number(s):
match = re.search(r'\d+', s)
return int(match.group()) if match else float('inf')
sort_methods = [
"None",
"Alphabetical (ASC)",
"Alphabetical (DESC)",
"Numerical (ASC)",
"Numerical (DESC)",
"Datetime (ASC)",
"Datetime (DESC)"
]
def sort_by(items, base_path='.', method=None):
def fullpath(x): return os.path.join(base_path, x)
def get_timestamp(path):
try:
return os.path.getmtime(path)
except FileNotFoundError:
return float('-inf')
if method == "Alphabetical (ASC)":
return sorted(items)
elif method == "Alphabetical (DESC)":
return sorted(items, reverse=True)
elif method == "Numerical (ASC)":
return sorted(items, key=lambda x: extract_first_number(os.path.splitext(x)[0]))
elif method == "Numerical (DESC)":
return sorted(items, key=lambda x: extract_first_number(os.path.splitext(x)[0]), reverse=True)
elif method == "Datetime (ASC)":
return sorted(items, key=lambda x: get_timestamp(fullpath(x)))
elif method == "Datetime (DESC)":
return sorted(items, key=lambda x: get_timestamp(fullpath(x)), reverse=True)
else:
return items
class LoadImagesFromDirBatch:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"directory": ("STRING", {"default": ""}),
},
"optional": {
"image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
"start_index": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff, "step": 1}),
"load_always": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"sort_method": (sort_methods,),
}
}
RETURN_TYPES = ("IMAGE", "MASK", "INT")
FUNCTION = "load_images"
CATEGORY = "image"
@classmethod
def IS_CHANGED(cls, **kwargs):
if 'load_always' in kwargs and kwargs['load_always']:
return float("NaN")
else:
return hash(frozenset(kwargs))
def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0, load_always=False, sort_method=None):
if not os.path.isdir(directory):
raise FileNotFoundError(f"Directory '{directory} cannot be found.'")
dir_files = os.listdir(directory)
if len(dir_files) == 0:
raise FileNotFoundError(f"No files in directory '{directory}'.")
# Filter files by extension
valid_extensions = ['.jpg', '.jpeg', '.png', '.webp']
if jxl:
valid_extensions.extend('.jxl')
dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)]
dir_files = sort_by(dir_files, directory, sort_method)
dir_files = [os.path.join(directory, x) for x in dir_files]
# start at start_index
dir_files = dir_files[start_index:]
images = []
masks = []
limit_images = False
if image_load_cap > 0:
limit_images = True
image_count = 0
has_non_empty_mask = False
for image_path in dir_files:
if os.path.isdir(image_path) and os.path.ex:
continue
if limit_images and image_count >= image_load_cap:
break
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)
has_non_empty_mask = True
else:
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
images.append(image)
masks.append(mask)
image_count += 1
if len(images) == 1:
return (images[0], masks[0], 1)
elif len(images) > 1:
image1 = images[0]
mask1 = None
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], "bilinear", "center").movedim(1, -1)
image1 = torch.cat((image1, image2), dim=0)
for mask2 in masks:
if has_non_empty_mask:
if image1.shape[1:3] != mask2.shape:
mask2 = torch.nn.functional.interpolate(mask2.unsqueeze(0).unsqueeze(0), size=(image1.shape[1], image1.shape[2]), mode='bilinear', align_corners=False)
mask2 = mask2.squeeze(0)
else:
mask2 = mask2.unsqueeze(0)
else:
mask2 = mask2.unsqueeze(0)
if mask1 is None:
mask1 = mask2
else:
mask1 = torch.cat((mask1, mask2), dim=0)
return (image1, mask1, len(images))
class LoadImagesFromDirList:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"directory": ("STRING", {"default": ""}),
},
"optional": {
"image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
"start_index": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
"load_always": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"sort_method": (sort_methods,),
}
}
RETURN_TYPES = ("IMAGE", "MASK", "STRING")
RETURN_NAMES = ("IMAGE", "MASK", "FILE PATH")
OUTPUT_IS_LIST = (True, True, True)
FUNCTION = "load_images"
CATEGORY = "image"
@classmethod
def IS_CHANGED(cls, **kwargs):
if 'load_always' in kwargs and kwargs['load_always']:
return float("NaN")
else:
return hash(frozenset(kwargs))
def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0, load_always=False, sort_method=None):
if not os.path.isdir(directory):
raise FileNotFoundError(f"Directory '{directory}' cannot be found.")
dir_files = os.listdir(directory)
if len(dir_files) == 0:
raise FileNotFoundError(f"No files in directory '{directory}'.")
# Filter files by extension
valid_extensions = ['.jpg', '.jpeg', '.png', '.webp']
if jxl:
valid_extensions.extend('.jxl')
dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)]
dir_files = sort_by(dir_files, directory, sort_method)
dir_files = [os.path.join(directory, x) for x in dir_files]
# start at start_index
dir_files = dir_files[start_index:]
images = []
masks = []
file_paths = []
limit_images = False
if image_load_cap > 0:
limit_images = True
image_count = 0
for image_path in dir_files:
if os.path.isdir(image_path) and os.path.ex:
continue
if limit_images and image_count >= image_load_cap:
break
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")
images.append(image)
masks.append(mask)
file_paths.append(str(image_path))
image_count += 1
return (images, masks, file_paths)
class LoadImageInspire:
@classmethod
def INPUT_TYPES(s):
input_dir = folder_paths.get_input_directory()
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
return {"required": {
"image": (sorted(files) + ["#DATA"], {"image_upload": True}),
"image_data": ("STRING", {"multiline": False}),
}
}
CATEGORY = "InspirePack/image"
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "load_image"
def load_image(self, image, image_data):
image_data = base64.b64decode(image_data.split(",")[1])
i = Image.open(BytesIO(image_data))
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")
return (image, mask.unsqueeze(0))
class ChangeImageBatchSize:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE",),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}),
"mode": (["simple"],)
}
}
CATEGORY = "InspirePack/Util"
RETURN_TYPES = ("IMAGE", )
FUNCTION = "doit"
@staticmethod
def resize_tensor(input_tensor, batch_size, mode):
if mode == "simple":
if len(input_tensor) < batch_size:
last_frame = input_tensor[-1].unsqueeze(0).expand(batch_size - len(input_tensor), -1, -1, -1)
output_tensor = torch.concat((input_tensor, last_frame), dim=0)
else:
output_tensor = input_tensor[:batch_size, :, :, :]
return output_tensor
else:
logging.warning(f"[Inspire Pack] ChangeImage(Latent)BatchSize: Unknown mode `{mode}` - ignored")
return input_tensor
@staticmethod
def doit(image, batch_size, mode):
res = ChangeImageBatchSize.resize_tensor(image, batch_size, mode)
return (res,)
class ChangeLatentBatchSize:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"latent": ("LATENT",),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}),
"mode": (["simple"],)
}
}
CATEGORY = "InspirePack/Util"
RETURN_TYPES = ("LATENT", )
FUNCTION = "doit"
@staticmethod
def doit(latent, batch_size, mode):
res_latent = latent.copy()
samples = res_latent['samples']
samples = ChangeImageBatchSize.resize_tensor(samples, batch_size, mode)
res_latent['samples'] = samples
return (res_latent,)
class ImageBatchSplitter:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"images": ("IMAGE",),
"split_count": ("INT", {"default": 4, "min": 0, "max": 50, "step": 1}),
},
}
RETURN_TYPES = ByPassTypeTuple(("IMAGE", ))
FUNCTION = "doit"
CATEGORY = "InspirePack/Util"
def doit(self, images, split_count):
cnt = min(split_count, len(images))
res = [image.unsqueeze(0) for image in images[:cnt]]
if split_count >= len(images):
lack_cnt = split_count - cnt + 1 # including remained
empty_image = empty_pil_tensor()
for x in range(0, lack_cnt):
res.append(empty_image)
elif cnt < len(images):
remained_cnt = len(images) - cnt
remained_image = images[-remained_cnt:]
res.append(remained_image)
return tuple(res)
class LatentBatchSplitter:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"latent": ("LATENT",),
"split_count": ("INT", {"default": 4, "min": 0, "max": 50, "step": 1}),
},
}
RETURN_TYPES = ByPassTypeTuple(("LATENT", ))
FUNCTION = "doit"
CATEGORY = "InspirePack/Util"
def doit(self, latent, split_count):
samples = latent['samples']
latent_base = latent.copy()
del latent_base['samples']
cnt = min(split_count, len(samples))
res = []
for single_samples in samples[:cnt]:
item = latent_base.copy()
item['samples'] = single_samples.unsqueeze(0)
res.append(item)
if split_count >= len(samples):
lack_cnt = split_count - cnt + 1 # including remained
item = latent_base.copy()
item['samples'] = empty_latent()
for x in range(0, lack_cnt):
res.append(item)
elif cnt < len(samples):
remained_cnt = len(samples) - cnt
remained_latent = latent_base.copy()
remained_latent['samples'] = samples[-remained_cnt:]
res.append(remained_latent)
return tuple(res)
def top_k_colors(image_tensor, k, min_pixels):
flattened_image = image_tensor.view(-1, image_tensor.size(-1))
unique_colors, counts = torch.unique(flattened_image, dim=0, return_counts=True)
sorted_counts, sorted_indices = torch.sort(counts, descending=True)
sorted_colors = unique_colors[sorted_indices]
filtered_colors = sorted_colors[sorted_counts >= min_pixels]
return filtered_colors[:k]
def create_mask(image_tensor, color):
mask_tensor = torch.zeros_like(image_tensor[:, :, :, 0])
mask_tensor = torch.where(torch.all(image_tensor == color, dim=-1, keepdim=False), 1, mask_tensor)
return mask_tensor
class ColorMapToMasks:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"color_map": ("IMAGE",),
"min_pixels": ("INT", {"default": 500, "min": 1, "max": 0xffffffffffffffff, "step": 1}),
"max_count": ("INT", {"default": 5, "min": 0, "max": 1000, "step": 1}),
},
}
RETURN_TYPES = ("MASK",)
FUNCTION = "doit"
CATEGORY = "InspirePack/Util"
def doit(self, color_map, max_count, min_pixels):
if len(color_map) > 0:
logging.warning("[Inspire Pack] ColorMapToMasks - Sure, here's the translation: `color_map` can only be a single image. Only the first image will be processed. If you want to utilize the remaining images, convert the Image Batch to an Image List.")
top_colors = top_k_colors(color_map[0], max_count, min_pixels)
masks = None
for color in top_colors:
this_mask = create_mask(color_map, color)
if masks is None:
masks = this_mask
else:
masks = torch.concat((masks, this_mask), dim=0)
if masks is None:
masks = torch.zeros_like(color_map[0, :, :, 0])
masks.unsqueeze(0)
return (masks,)
class SelectNthMask:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"masks": ("MASK",),
"idx": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
},
}
RETURN_TYPES = ("MASK",)
FUNCTION = "doit"
CATEGORY = "InspirePack/Util"
def doit(self, masks, idx):
return (masks[idx].unsqueeze(0),)
NODE_CLASS_MAPPINGS = {
"LoadImagesFromDir //Inspire": LoadImagesFromDirBatch,
"LoadImageListFromDir //Inspire": LoadImagesFromDirList,
"LoadImage //Inspire": LoadImageInspire,
"ChangeImageBatchSize //Inspire": ChangeImageBatchSize,
"ChangeLatentBatchSize //Inspire": ChangeLatentBatchSize,
"ImageBatchSplitter //Inspire": ImageBatchSplitter,
"LatentBatchSplitter //Inspire": LatentBatchSplitter,
"ColorMapToMasks //Inspire": ColorMapToMasks,
"SelectNthMask //Inspire": SelectNthMask
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LoadImagesFromDir //Inspire": "Load Image Batch From Dir (Inspire)",
"LoadImageListFromDir //Inspire": "Load Image List From Dir (Inspire)",
"LoadImage //Inspire": "Load Image (Inspire)",
"ChangeImageBatchSize //Inspire": "Change Image Batch Size (Inspire)",
"ChangeLatentBatchSize //Inspire": "Change Latent Batch Size (Inspire)",
"ImageBatchSplitter //Inspire": "Image Batch Splitter (Inspire)",
"LatentBatchSplitter //Inspire": "Latent Batch Splitter (Inspire)",
"ColorMapToMasks //Inspire": "Color Map To Masks (Inspire)",
"SelectNthMask //Inspire": "Select Nth Mask (Inspire)"
}

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custom_nodes/comfyui-inspire-pack/inspire/inspire_server.py Normal file
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import random
import nodes
import server
from enum import Enum
from . import prompt_support
from aiohttp import web
from . import backend_support
from .libs import common
import logging
max_seed = 2**32 - 1
@server.PromptServer.instance.routes.get("/inspire/prompt_builder")
def prompt_builder(request):
result = {"presets": []}
if "category" in request.rel_url.query:
category = request.rel_url.query["category"]
if category in prompt_support.prompt_builder_preset:
result['presets'] = prompt_support.prompt_builder_preset[category]
return web.json_response(result)
@server.PromptServer.instance.routes.get("/inspire/cache/remove")
def cache_remove(request):
if "key" in request.rel_url.query:
key = request.rel_url.query["key"]
del backend_support.cache[key]
return web.Response(status=200)
@server.PromptServer.instance.routes.get("/inspire/cache/clear")
def cache_clear(request):
backend_support.cache.clear()
return web.Response(status=200)
@server.PromptServer.instance.routes.get("/inspire/cache/list")
def cache_refresh(request):
return web.Response(text=backend_support.ShowCachedInfo.get_data(), status=200)
@server.PromptServer.instance.routes.post("/inspire/cache/settings")
async def set_cache_settings(request):
data = await request.text()
try:
backend_support.ShowCachedInfo.set_cache_settings(data)
return web.Response(text='OK', status=200)
except Exception as e:
return web.Response(text=f"{e}", status=500)
class SGmode(Enum):
FIX = 1
INCR = 2
DECR = 3
RAND = 4
class SeedGenerator:
def __init__(self, base_value, action):
self.base_value = base_value
if action == "fixed" or action == "increment" or action == "decrement" or action == "randomize":
self.action = SGmode.FIX
elif action == 'increment for each node':
self.action = SGmode.INCR
elif action == 'decrement for each node':
self.action = SGmode.DECR
elif action == 'randomize for each node':
self.action = SGmode.RAND
def next(self):
seed = self.base_value
if self.action == SGmode.INCR:
self.base_value += 1
if self.base_value > max_seed:
self.base_value = 0
elif self.action == SGmode.DECR:
self.base_value -= 1
if self.base_value < 0:
self.base_value = max_seed
elif self.action == SGmode.RAND:
self.base_value = random.randint(0, max_seed)
return seed
def control_seed(v):
action = v['inputs']['action']
value = v['inputs']['value']
if action == 'increment' or action == 'increment for each node':
value += 1
if value > max_seed:
value = 0
elif action == 'decrement' or action == 'decrement for each node':
value -= 1
if value < 0:
value = max_seed
elif action == 'randomize' or action == 'randomize for each node':
value = random.randint(0, max_seed)
v['inputs']['value'] = value
return value
def prompt_seed_update(json_data):
try:
widget_idx_map = json_data['extra_data']['extra_pnginfo']['workflow']['widget_idx_map']
except Exception:
return False, None
value = None
mode = None
node = None
action = None
for k, v in json_data['prompt'].items():
if 'class_type' not in v:
continue
cls = v['class_type']
if cls == 'GlobalSeed //Inspire':
mode = v['inputs']['mode']
action = v['inputs']['action']
value = v['inputs']['value']
node = k, v
# control before generated
if mode is not None and mode:
value = control_seed(node[1])
if value is not None:
seed_generator = SeedGenerator(value, action)
for k, v in json_data['prompt'].items():
for k2, v2 in v['inputs'].items():
if isinstance(v2, str) and '$GlobalSeed.value$' in v2:
v['inputs'][k2] = v2.replace('$GlobalSeed.value$', str(value))
if k not in widget_idx_map or ('seed' not in widget_idx_map[k] and 'noise_seed' not in widget_idx_map[k]):
continue
if 'seed' in v['inputs']:
if isinstance(v['inputs']['seed'], int):
v['inputs']['seed'] = seed_generator.next()
if 'noise_seed' in v['inputs']:
if isinstance(v['inputs']['noise_seed'], int):
v['inputs']['noise_seed'] = seed_generator.next()
for k2, v2 in v['inputs'].items():
if isinstance(v2, str) and '$GlobalSeed.value$' in v2:
v['inputs'][k2] = v2.replace('$GlobalSeed.value$', str(value))
# control after generated
if mode is not None and not mode:
control_seed(node[1])
return value is not None, mode
def workflow_seed_update(json_data, mode):
nodes = json_data['extra_data']['extra_pnginfo']['workflow']['nodes']
widget_idx_map = json_data['extra_data']['extra_pnginfo']['workflow']['widget_idx_map']
prompt = json_data['prompt']
updated_seed_map = {}
value = None
for node in nodes:
node_id = str(node['id'])
if node_id in prompt:
if node['type'] == 'GlobalSeed //Inspire':
if mode is True:
node['widgets_values'][3] = node['widgets_values'][0]
node['widgets_values'][0] = prompt[node_id]['inputs']['value']
node['widgets_values'][2] = 'fixed'
value = prompt[node_id]['inputs']['value']
elif node_id in widget_idx_map:
widget_idx = None
seed = None
if 'noise_seed' in prompt[node_id]['inputs']:
seed = prompt[node_id]['inputs']['noise_seed']
widget_idx = widget_idx_map[node_id].get('noise_seed')
elif 'seed' in prompt[node_id]['inputs']:
seed = prompt[node_id]['inputs']['seed']
widget_idx = widget_idx_map[node_id].get('seed')
if widget_idx is not None:
node['widgets_values'][widget_idx] = seed
updated_seed_map[node_id] = seed
server.PromptServer.instance.send_sync("inspire-global-seed", {"value": value, "seed_map": updated_seed_map})
def prompt_sampler_update(json_data):
try:
widget_idx_map = json_data['extra_data']['extra_pnginfo']['workflow']['widget_idx_map']
except Exception:
return None
nodes = json_data['extra_data']['extra_pnginfo']['workflow']['nodes']
prompt = json_data['prompt']
sampler_name = None
scheduler = None
for v in prompt.values():
cls = v.get('class_type')
if cls == 'GlobalSampler //Inspire':
sampler_name = v['inputs']['sampler_name']
scheduler = v['inputs']['scheduler']
if sampler_name is None:
return
for node in nodes:
cls = node.get('type')
if cls == 'GlobalSampler //Inspire' or cls is None:
continue
node_id = str(node['id'])
if node_id in prompt and node_id in widget_idx_map:
sampler_widget_idx = widget_idx_map[node_id].get('sampler_name')
scheduler_widget_idx = widget_idx_map[node_id].get('scheduler')
prompt_inputs = prompt[node_id]['inputs']
if ('sampler_name' in prompt_inputs and 'scheduler' in prompt_inputs and
isinstance(prompt_inputs['sampler_name'], str) and 'scheduler' in prompt_inputs):
if sampler_widget_idx is not None:
prompt_inputs['sampler_name'] = sampler_name
node['widgets_values'][sampler_widget_idx] = sampler_name
server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": node_id, "widget_name": 'sampler_name', "type": "text", "data": sampler_name})
if scheduler_widget_idx is not None:
prompt_inputs['scheduler'] = scheduler
node['widgets_values'][scheduler_widget_idx] = scheduler
server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": node_id, "widget_name": 'scheduler', "type": "text", "data": scheduler})
def workflow_loadimage_update(json_data):
prompt = json_data['prompt']
for v in prompt.values():
if 'class_type' in v and v['class_type'] == 'LoadImage //Inspire':
v['inputs']['image'] = "#DATA"
def populate_wildcards(json_data):
prompt = json_data['prompt']
if 'ImpactWildcardProcessor' in nodes.NODE_CLASS_MAPPINGS:
if not hasattr(nodes.NODE_CLASS_MAPPINGS['ImpactWildcardProcessor'], 'process'):
logging.warning("[Inspire Pack] Your Impact Pack is outdated. Please update to the latest version.")
return
wildcard_process = nodes.NODE_CLASS_MAPPINGS['ImpactWildcardProcessor'].process
updated_widget_values = {}
mbp_updated_widget_values = {}
for k, v in prompt.items():
if 'class_type' in v and v['class_type'] == 'WildcardEncode //Inspire':
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.warning("[Inspire Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ")
continue
except:
continue
else:
input_seed = int(inputs['seed'])
inputs['populated_text'] = wildcard_process(text=inputs['wildcard_text'], seed=input_seed)
inputs['mode'] = 'reproduce'
server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "populated_text", "type": "text", "data": inputs['populated_text']})
updated_widget_values[k] = inputs['populated_text']
if inputs['mode'] == 'reproduce':
server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "mode", "type": "text", "value": 'populate'})
elif 'class_type' in v and v['class_type'] == 'MakeBasicPipe //Inspire':
inputs = v['inputs']
if inputs['wildcard_mode'] == 'populate' and (isinstance(inputs['positive_populated_text'], str) or isinstance(inputs['negative_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.warning("[Inspire Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ")
continue
except:
continue
else:
input_seed = int(inputs['seed'])
if isinstance(inputs['positive_populated_text'], str):
inputs['positive_populated_text'] = wildcard_process(text=inputs['positive_wildcard_text'], seed=input_seed)
server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "positive_populated_text", "type": "text", "data": inputs['positive_populated_text']})
if isinstance(inputs['negative_populated_text'], str):
inputs['negative_populated_text'] = wildcard_process(text=inputs['negative_wildcard_text'], seed=input_seed)
server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "negative_populated_text", "type": "text", "data": inputs['negative_populated_text']})
inputs['wildcard_mode'] = 'reproduce'
mbp_updated_widget_values[k] = inputs['positive_populated_text'], inputs['negative_populated_text']
if inputs['wildcard_mode'] == 'reproduce':
server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "wildcard_mode", "type": "text", "value": 'populate'})
if 'extra_data' in json_data and 'extra_pnginfo' in json_data['extra_data']:
extra_pnginfo = json_data['extra_data']['extra_pnginfo']
if 'workflow' in extra_pnginfo and extra_pnginfo['workflow'] is not None and 'nodes' in extra_pnginfo['workflow']:
for node in extra_pnginfo['workflow']['nodes']:
key = str(node['id'])
if key in updated_widget_values:
node['widgets_values'][3] = updated_widget_values[key]
node['widgets_values'][4] = 'reproduce'
if key in mbp_updated_widget_values:
node['widgets_values'][7] = mbp_updated_widget_values[key][0]
node['widgets_values'][8] = mbp_updated_widget_values[key][1]
node['widgets_values'][5] = 'reproduce'
def force_reset_useless_params(json_data):
prompt = json_data['prompt']
for k, v in prompt.items():
if 'class_type' in v and v['class_type'] == 'PromptBuilder //Inspire':
v['inputs']['category'] = '#PLACEHOLDER'
return json_data
def clear_unused_node_changed_cache(json_data):
prompt = json_data['prompt']
unused = []
for x in common.changed_cache.keys():
if x not in prompt:
unused.append(x)
for x in unused:
del common.changed_cache[x]
del common.changed_count_cache[x]
return json_data
def onprompt(json_data):
prompt_support.list_counter_map = {}
is_changed, mode = prompt_seed_update(json_data)
if is_changed:
workflow_seed_update(json_data, mode)
prompt_sampler_update(json_data)
workflow_loadimage_update(json_data)
populate_wildcards(json_data)
force_reset_useless_params(json_data)
clear_unused_node_changed_cache(json_data)
return json_data
server.PromptServer.instance.add_on_prompt_handler(onprompt)
NODE_CLASS_MAPPINGS = {}
NODE_DISPLAY_NAME_MAPPINGS = {}

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custom_nodes/comfyui-inspire-pack/inspire/libs/common.py Normal file
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import comfy
import nodes
from . import utils
import logging
from server import PromptServer
ADDITIONAL_SCHEDULERS = ['AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]', 'LTXV[default]', 'OSS FLUX', 'OSS Wan', 'OSS Chroma']
def get_schedulers():
return list(comfy.samplers.SCHEDULER_HANDLERS) + ADDITIONAL_SCHEDULERS
def impact_sampling(*args, **kwargs):
if 'RegionalSampler' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack',
"'Impact Pack' extension is required.")
raise Exception("[ERROR] You need to install 'ComfyUI-Impact-Pack'")
return nodes.NODE_CLASS_MAPPINGS['RegionalSampler'].separated_sample(*args, **kwargs)
changed_count_cache = {}
changed_cache = {}
def changed_value(uid):
v = changed_count_cache.get(uid, 0)
changed_count_cache[uid] = v + 1
return v + 1
def not_changed_value(uid):
return changed_count_cache.get(uid, 0)
def is_changed(uid, value):
if uid not in changed_cache or changed_cache[uid] != value:
res = changed_value(uid)
else:
res = not_changed_value(uid)
changed_cache[uid] = value
logging.info(f"keys: {changed_cache.keys()}")
return res
def update_node_status(node, text, progress=None):
if PromptServer.instance.client_id is None:
return
PromptServer.instance.send_sync("inspire/update_status", {
"node": node,
"progress": progress,
"text": text
}, PromptServer.instance.client_id)
class ListWrapper:
def __init__(self, data, aux=None):
if isinstance(data, ListWrapper):
self._data = data
if aux is None:
self.aux = data.aux
else:
self.aux = aux
else:
self._data = list(data)
self.aux = aux
def __getitem__(self, index):
if isinstance(index, slice):
return ListWrapper(self._data[index], self.aux)
else:
return self._data[index]
def __setitem__(self, index, value):
self._data[index] = value
def __len__(self):
return len(self._data)
def __repr__(self):
return f"ListWrapper({self._data}, aux={self.aux})"

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import itertools
from typing import Optional
import numpy as np
import torch
from PIL import Image, ImageDraw
import math
import cv2
import folder_paths
import logging
def apply_variation_noise(latent_image, noise_device, variation_seed, variation_strength, mask=None, variation_method='linear'):
latent_size = latent_image.size()
latent_size_1batch = [1, latent_size[1], latent_size[2], latent_size[3]]
if noise_device == "cpu":
variation_generator = torch.manual_seed(variation_seed)
else:
torch.cuda.manual_seed(variation_seed)
variation_generator = None
variation_latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout,
generator=variation_generator, device=noise_device)
variation_noise = variation_latent.expand(latent_image.size()[0], -1, -1, -1)
if variation_strength == 0:
return latent_image
elif mask is None:
result = (1 - variation_strength) * latent_image + variation_strength * variation_noise
else:
# this seems precision is not enough when variation_strength is 0.0
mixed_noise = mix_noise(latent_image, variation_noise, variation_strength, variation_method=variation_method)
result = (mask == 1).float() * mixed_noise + (mask == 0).float() * latent_image
return result
# CREDIT: https://github.com/BlenderNeko/ComfyUI_Noise/blob/afb14757216257b12268c91845eac248727a55e2/nodes.py#L68
# https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
def slerp(val, low, high):
dims = low.shape
low = low.reshape(dims[0], -1)
high = high.reshape(dims[0], -1)
low_norm = low/torch.norm(low, dim=1, keepdim=True)
high_norm = high/torch.norm(high, dim=1, keepdim=True)
low_norm[low_norm != low_norm] = 0.0
high_norm[high_norm != high_norm] = 0.0
omega = torch.acos((low_norm*high_norm).sum(1))
so = torch.sin(omega)
res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
return res.reshape(dims)
def mix_noise(from_noise, to_noise, strength, variation_method):
to_noise = to_noise.to(from_noise.device)
if variation_method == 'slerp':
mixed_noise = slerp(strength, from_noise, to_noise)
else:
# linear
mixed_noise = (1 - strength) * from_noise + strength * to_noise
# NOTE: Since the variance of the Gaussian noise in mixed_noise has changed, it must be corrected through scaling.
scale_factor = math.sqrt((1 - strength) ** 2 + strength ** 2)
mixed_noise /= scale_factor
return mixed_noise
def prepare_noise(latent_image, seed, noise_inds=None, noise_device="cpu", incremental_seed_mode="comfy", variation_seed=None, variation_strength=None, variation_method="linear"):
"""
creates random noise given a latent image and a seed.
optional arg skip can be used to skip and discard x number of noise generations for a given seed
"""
latent_size = latent_image.size()
latent_size_1batch = [1, latent_size[1], latent_size[2], latent_size[3]]
if variation_strength is not None and variation_strength > 0 or incremental_seed_mode.startswith("variation str inc"):
if noise_device == "cpu":
variation_generator = torch.manual_seed(variation_seed)
else:
torch.cuda.manual_seed(variation_seed)
variation_generator = None
variation_latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout,
generator=variation_generator, device=noise_device)
else:
variation_latent = None
def apply_variation(input_latent, strength_up=None):
if variation_latent is None:
return input_latent
else:
strength = variation_strength
if strength_up is not None:
strength += strength_up
variation_noise = variation_latent.expand(input_latent.size()[0], -1, -1, -1)
mixed_noise = mix_noise(input_latent, variation_noise, strength, variation_method)
return mixed_noise
# method: incremental seed batch noise
if noise_inds is None and incremental_seed_mode == "incremental":
batch_cnt = latent_size[0]
latents = None
for i in range(batch_cnt):
if noise_device == "cpu":
generator = torch.manual_seed(seed+i)
else:
torch.cuda.manual_seed(seed+i)
generator = None
latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout,
generator=generator, device=noise_device)
latent = apply_variation(latent)
if latents is None:
latents = latent
else:
latents = torch.cat((latents, latent), dim=0)
return latents
# method: incremental variation batch noise
elif noise_inds is None and incremental_seed_mode.startswith("variation str inc"):
batch_cnt = latent_size[0]
latents = None
for i in range(batch_cnt):
if noise_device == "cpu":
generator = torch.manual_seed(seed)
else:
torch.cuda.manual_seed(seed)
generator = None
latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout,
generator=generator, device=noise_device)
step = float(incremental_seed_mode[18:])
latent = apply_variation(latent, step*i)
if latents is None:
latents = latent
else:
latents = torch.cat((latents, latent), dim=0)
return latents
# method: comfy batch noise
if noise_device == "cpu":
generator = torch.manual_seed(seed)
else:
torch.cuda.manual_seed(seed)
generator = None
if noise_inds is None:
latents = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout,
generator=generator, device=noise_device)
latents = apply_variation(latents)
return latents
unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
noises = []
for i in range(unique_inds[-1] + 1):
noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout,
generator=generator, device=noise_device)
if i in unique_inds:
noises.append(noise)
noises = [noises[i] for i in inverse]
noises = torch.cat(noises, axis=0)
return noises
def pil2tensor(image):
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
def empty_pil_tensor(w=64, h=64):
image = Image.new("RGB", (w, h))
draw = ImageDraw.Draw(image)
draw.rectangle((0, 0, w-1, h-1), fill=(0, 0, 0))
return pil2tensor(image)
def try_install_custom_node(custom_node_url, msg):
try:
import cm_global
cm_global.try_call(api='cm.try-install-custom-node',
sender="Inspire Pack", custom_node_url=custom_node_url, msg=msg)
except Exception as e: # noqa: F841
logging.error(msg)
logging.error("[Inspire Pack] ComfyUI-Manager is outdated. The custom node installation feature is not available.")
def empty_latent():
return torch.zeros([1, 4, 8, 8])
# wildcard trick is taken from pythongossss's
class AnyType(str):
def __ne__(self, __value: object) -> bool:
return False
any_typ = AnyType("*")
# 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 TaggedCache:
def __init__(self, tag_settings: Optional[dict]=None):
self._tag_settings = tag_settings or {} # tag cache size
self._data = {}
def __getitem__(self, key):
for tag_data in self._data.values():
if key in tag_data:
return tag_data[key]
raise KeyError(f'Key `{key}` does not exist')
def __setitem__(self, key, value: tuple):
# value: (tag: str, (islist: bool, data: *))
# if key already exists, pop old value
for tag_data in self._data.values():
if key in tag_data:
tag_data.pop(key, None)
break
tag = value[0]
if tag not in self._data:
try:
from cachetools import LRUCache
default_size = 20
if 'ckpt' in tag:
default_size = 5
elif tag in ['latent', 'image']:
default_size = 100
self._data[tag] = LRUCache(maxsize=self._tag_settings.get(tag, default_size))
except (ImportError, ModuleNotFoundError):
# TODO: implement a simple lru dict
self._data[tag] = {}
self._data[tag][key] = value
def __delitem__(self, key):
for tag_data in self._data.values():
if key in tag_data:
del tag_data[key]
return
raise KeyError(f'Key `{key}` does not exist')
def __contains__(self, key):
return any(key in tag_data for tag_data in self._data.values())
def items(self):
yield from itertools.chain(*map(lambda x :x.items(), self._data.values()))
def get(self, key, default=None):
"""D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None."""
for tag_data in self._data.values():
if key in tag_data:
return tag_data[key]
return default
def clear(self):
# clear all cache
self._data = {}
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 dilate_mask(mask: torch.Tensor, dilation_factor: float) -> torch.Tensor:
"""Dilate a mask using a square kernel with a given dilation factor."""
kernel_size = int(dilation_factor * 2) + 1
kernel = np.ones((abs(kernel_size), abs(kernel_size)), np.uint8)
masks = make_3d_mask(mask).numpy()
dilated_masks = []
for m in masks:
if dilation_factor > 0:
m2 = cv2.dilate(m, kernel, iterations=1)
else:
m2 = cv2.erode(m, kernel, iterations=1)
dilated_masks.append(torch.from_numpy(m2))
return torch.stack(dilated_masks)
def flatten_non_zero_override(masks: torch.Tensor):
"""
flatten multiple layer mask tensor to 1 layer mask tensor.
Override the lower layer with the tensor from the upper layer, but only override non-zero values.
:param masks: 3d mask
:return: flatten mask
"""
final_mask = masks[0]
for i in range(1, masks.size(0)):
non_zero_mask = masks[i] != 0
final_mask[non_zero_mask] = masks[i][non_zero_mask]
return final_mask
def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions):
for full_folder_path in full_folder_paths:
folder_paths.add_model_folder_path(folder_name, full_folder_path)
if folder_name in folder_paths.folder_names_and_paths:
current_paths, current_extensions = folder_paths.folder_names_and_paths[folder_name]
updated_extensions = current_extensions | extensions
folder_paths.folder_names_and_paths[folder_name] = (current_paths, updated_extensions)
else:
folder_paths.folder_names_and_paths[folder_name] = (full_folder_paths, extensions)

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custom_nodes/comfyui-inspire-pack/inspire/list_nodes.py Normal file
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import logging
from comfy_execution.graph_utils import GraphBuilder, is_link
from .libs.utils import any_typ
from .libs.common import update_node_status, ListWrapper
class FloatRange:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"start": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, 'step': 0.000000001}),
"stop": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, 'step': 0.000000001}),
"step": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 100.0, 'step': 0.000000001}),
"limit": ("INT", {"default": 100, "min": 2, "max": 4096, "step": 1}),
"ensure_end": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
}
}
RETURN_TYPES = ("FLOAT",)
OUTPUT_IS_LIST = (True,)
FUNCTION = "doit"
CATEGORY = "InspirePack/List"
def doit(self, start, stop, step, limit, ensure_end):
if start == stop or step == 0:
return ([start], )
reverse = False
if start > stop:
reverse = True
start, stop = stop, start
res = []
x = start
last = x
while x <= stop and limit > 0:
res.append(x)
last = x
limit -= 1
x += step
if ensure_end and last != stop:
if len(res) >= limit:
res.pop()
res.append(stop)
if reverse:
res.reverse()
return (res, )
class WorklistToItemList:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"item": (any_typ, ),
}
}
INPUT_IS_LIST = True
RETURN_TYPES = ("ITEM_LIST",)
RETURN_NAMES = ("item_list",)
FUNCTION = "doit"
DESCRIPTION = "The list in ComfyUI allows for repeated execution of a sub-workflow.\nThis groups these repetitions (a.k.a. list) into a single ITEM_LIST output.\nITEM_LIST can then be used in ForeachList."
CATEGORY = "InspirePack/List"
def doit(self, item):
return (item, )
# Loop nodes are implemented based on BadCafeCode's reference loop implementation
# https://github.com/BadCafeCode/execution-inversion-demo-comfyui/blob/main/flow_control.py
class ForeachListBegin:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"item_list": ("ITEM_LIST", {"tooltip": "ITEM_LIST containing items to be processed iteratively."}),
},
"optional": {
"initial_input": (any_typ, {"tooltip": "If initial_input is omitted, the first item in item_list is used as the initial value, and the processing starts from the second item in item_list."}),
}
}
RETURN_TYPES = ("FOREACH_LIST_CONTROL", "ITEM_LIST", any_typ, any_typ)
RETURN_NAMES = ("flow_control", "remained_list", "item", "intermediate_output")
OUTPUT_TOOLTIPS = (
"Pass ForeachListEnd as is to indicate the end of the iteration.",
"Output the ITEM_LIST containing the remaining items during the iteration, passing ForeachListEnd as is to indicate the end of the iteration.",
"Output the current item during the iteration.",
"Output the intermediate results during the iteration.")
FUNCTION = "doit"
DESCRIPTION = "A starting node for performing iterative tasks by retrieving items one by one from the ITEM_LIST.\nGenerate a new intermediate_output using item and intermediate_output as inputs, then connect it to ForeachListEnd.\nNOTE:If initial_input is omitted, the first item in item_list is used as the initial value, and the processing starts from the second item in item_list."
CATEGORY = "InspirePack/List"
def doit(self, item_list, initial_input=None):
if initial_input is None:
initial_input = item_list[0]
item_list = item_list[1:]
if len(item_list) > 0:
next_list = ListWrapper(item_list[1:])
next_item = item_list[0]
else:
next_list = ListWrapper([])
next_item = None
if next_list.aux is None:
next_list.aux = len(item_list), None
return "stub", next_list, next_item, initial_input
class ForeachListEnd:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"flow_control": ("FOREACH_LIST_CONTROL", {"rawLink": True, "tooltip": "Directly connect the output of ForeachListBegin, the starting node of the iteration."}),
"remained_list": ("ITEM_LIST", {"tooltip":"Directly connect the output of ForeachListBegin, the starting node of the iteration."}),
"intermediate_output": (any_typ, {"tooltip":"Connect the intermediate outputs processed within the iteration here."}),
},
"hidden": {
"dynprompt": "DYNPROMPT",
"unique_id": "UNIQUE_ID",
}
}
RETURN_TYPES = (any_typ,)
RETURN_NAMES = ("result",)
OUTPUT_TOOLTIPS = ("This is the final output value.",)
FUNCTION = "doit"
DESCRIPTION = "A end node for performing iterative tasks by retrieving items one by one from the ITEM_LIST.\nNOTE:Directly connect the outputs of ForeachListBegin to 'flow_control' and 'remained_list'."
CATEGORY = "InspirePack/List"
def explore_dependencies(self, node_id, dynprompt, upstream):
node_info = dynprompt.get_node(node_id)
if "inputs" not in node_info:
return
for k, v in node_info["inputs"].items():
if is_link(v):
parent_id = v[0]
if parent_id not in upstream:
upstream[parent_id] = []
self.explore_dependencies(parent_id, dynprompt, upstream)
upstream[parent_id].append(node_id)
def collect_contained(self, node_id, upstream, contained):
if node_id not in upstream:
return
for child_id in upstream[node_id]:
if child_id not in contained:
contained[child_id] = True
self.collect_contained(child_id, upstream, contained)
def doit(self, flow_control, remained_list, intermediate_output, dynprompt, unique_id):
if hasattr(remained_list, "aux"):
if remained_list.aux[1] is None:
remained_list.aux = (remained_list.aux[0], unique_id)
update_node_status(remained_list.aux[1], f"{(remained_list.aux[0]-len(remained_list))}/{remained_list.aux[0]} steps", (remained_list.aux[0]-len(remained_list))/remained_list.aux[0])
else:
logging.warning("[Inspire Pack] ForeachListEnd: `remained_list` did not come from ForeachList.")
if len(remained_list) == 0:
return (intermediate_output,)
# We want to loop
upstream = {}
# Get the list of all nodes between the open and close nodes
self.explore_dependencies(unique_id, dynprompt, upstream)
contained = {}
open_node = flow_control[0]
self.collect_contained(open_node, upstream, contained)
contained[unique_id] = True
contained[open_node] = True
# We'll use the default prefix, but to avoid having node names grow exponentially in size,
# we'll use "Recurse" for the name of the recursively-generated copy of this node.
graph = GraphBuilder()
for node_id in contained:
original_node = dynprompt.get_node(node_id)
node = graph.node(original_node["class_type"], "Recurse" if node_id == unique_id else node_id)
node.set_override_display_id(node_id)
for node_id in contained:
original_node = dynprompt.get_node(node_id)
node = graph.lookup_node("Recurse" if node_id == unique_id else node_id)
for k, v in original_node["inputs"].items():
if is_link(v) and v[0] in contained:
parent = graph.lookup_node(v[0])
node.set_input(k, parent.out(v[1]))
else:
node.set_input(k, v)
new_open = graph.lookup_node(open_node)
new_open.set_input("item_list", remained_list)
new_open.set_input("initial_input", intermediate_output)
my_clone = graph.lookup_node("Recurse" )
result = (my_clone.out(0),)
return {
"result": result,
"expand": graph.finalize(),
}
class DropItems:
@classmethod
def INPUT_TYPES(s):
return {
"required": { "item_list": ("ITEM_LIST", {"tooltip":"Directly connect the output of ForeachListBegin, the starting node of the iteration."}), },
}
RETURN_TYPES = (any_typ,)
RETURN_NAMES = ("ITEM_LIST",)
OUTPUT_TOOLTIPS = ("This is the final output value.",)
FUNCTION = "doit"
DESCRIPTION = ""
CATEGORY = "InspirePack/List"
def doit(self, item_list):
l = ListWrapper([])
if hasattr(item_list, 'aux'):
l.aux = item_list.aux
else:
logging.warning("[Inspire Pack] DropItems: `item_list` did not come from ForeachList.")
return (l,)
NODE_CLASS_MAPPINGS = {
"FloatRange //Inspire": FloatRange,
"WorklistToItemList //Inspire": WorklistToItemList,
"ForeachListBegin //Inspire": ForeachListBegin,
"ForeachListEnd //Inspire": ForeachListEnd,
"DropItems //Inspire": DropItems,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"FloatRange //Inspire": "Float Range (Inspire)",
"WorklistToItemList //Inspire": "Worklist To Item List (Inspire)",
"ForeachListBegin //Inspire": "▶Foreach List (Inspire)",
"ForeachListEnd //Inspire": "Foreach List◀ (Inspire)",
"DropItems //Inspire": "Drop Items (Inspire)",
}

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import nodes
import folder_paths
import os
import server
from .libs import utils
from . import backend_support
from comfy import sdxl_clip
import logging
model_preset = {
# base
"SD1.5": ("ip-adapter_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
"SD1.5 Light v11": ("ip-adapter_sd15_light_v11", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
"SD1.5 Light": ("ip-adapter_sd15_light", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
"SD1.5 Plus": ("ip-adapter-plus_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
"SD1.5 Plus Face": ("ip-adapter-plus-face_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
"SD1.5 Full Face": ("ip-adapter-full-face_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
"SD1.5 ViT-G": ("ip-adapter_sd15_vit-G", "CLIP-ViT-bigG-14-laion2B-39B-b160k", None, False),
"SDXL": ("ip-adapter_sdxl", "CLIP-ViT-bigG-14-laion2B-39B-b160k", None, False),
"SDXL ViT-H": ("ip-adapter_sdxl_vit-h", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
"SDXL Plus ViT-H": ("ip-adapter-plus_sdxl_vit-h", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
"SDXL Plus Face ViT-H": ("ip-adapter-plus-face_sdxl_vit-h", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
"Kolors Plus": ("Kolors-IP-Adapter-Plus", "clip-vit-large-patch14-336", None, False),
# faceid
"SD1.5 FaceID": ("ip-adapter-faceid_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", "ip-adapter-faceid_sd15_lora", True),
"SD1.5 FaceID Plus v2": ("ip-adapter-faceid-plusv2_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", "ip-adapter-faceid-plusv2_sd15_lora", True),
"SD1.5 FaceID Plus": ("ip-adapter-faceid-plus_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", "ip-adapter-faceid-plus_sd15_lora", True),
"SD1.5 FaceID Portrait v11": ("ip-adapter-faceid-portrait-v11_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, True),
"SD1.5 FaceID Portrait": ("ip-adapter-faceid-portrait_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, True),
"SDXL FaceID": ("ip-adapter-faceid_sdxl", "CLIP-ViT-H-14-laion2B-s32B-b79K", "ip-adapter-faceid_sdxl_lora", True),
"SDXL FaceID Plus v2": ("ip-adapter-faceid-plusv2_sdxl", "CLIP-ViT-H-14-laion2B-s32B-b79K", "ip-adapter-faceid-plusv2_sdxl_lora", True),
"SDXL FaceID Portrait": ("ip-adapter-faceid-portrait_sdxl", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, True),
"SDXL FaceID Portrait unnorm": ("ip-adapter-faceid-portrait_sdxl_unnorm", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, True),
"Kolors FaceID Plus": ("Kolors-IP-Adapter-FaceID-Plus", "clip-vit-large-patch14-336", None, True),
# composition
"SD1.5 Plus Composition": ("ip-adapter_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
"SDXL Plus Composition": ("ip-adapter_sdxl", "CLIP-ViT-bigG-14-laion2B-39B-b160k", None, False),
}
def lookup_model(model_dir, name):
if name is None:
return None, "N/A"
names = [(os.path.splitext(os.path.basename(x))[0], x) for x in folder_paths.get_filename_list(model_dir)]
resolved_name = [y for x, y in names if x == name]
if len(resolved_name) > 0:
return resolved_name[0], "OK"
else:
logging.error(f"[Inspire Pack] IPAdapterModelHelper: The `{name}` model file does not exist in `{model_dir}` model dir.")
return None, "FAIL"
class IPAdapterModelHelper:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"preset": (list(model_preset.keys()),),
"lora_strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
"lora_strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
"insightface_provider": (["CPU", "CUDA", "ROCM"], ),
"cache_mode": (["insightface only", "clip_vision only", "all", "none"], {"default": "insightface only"}),
},
"optional": {
"clip": ("CLIP",),
"insightface_model_name": (['buffalo_l', 'antelopev2'],),
},
"hidden": {"unique_id": "UNIQUE_ID"}
}
RETURN_TYPES = ("IPADAPTER_PIPE", "IPADAPTER", "CLIP_VISION", "INSIGHTFACE", "MODEL", "CLIP", "STRING", "STRING")
RETURN_NAMES = ("IPADAPTER_PIPE", "IPADAPTER", "CLIP_VISION", "INSIGHTFACE", "MODEL", "CLIP", "insightface_cache_key", "clip_vision_cache_key")
FUNCTION = "doit"
CATEGORY = "InspirePack/models"
def doit(self, model, preset, lora_strength_model, lora_strength_clip, insightface_provider, clip=None, cache_mode="none", unique_id=None, insightface_model_name='buffalo_l'):
if 'IPAdapter' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/cubiq/ComfyUI_IPAdapter_plus',
"To use 'IPAdapterModelHelper' node, 'ComfyUI IPAdapter Plus' extension is required.")
raise Exception("[ERROR] To use IPAdapterModelHelper, you need to install 'ComfyUI IPAdapter Plus'")
is_sdxl_preset = 'SDXL' in preset
if clip is not None:
is_sdxl_model = isinstance(clip.tokenizer, sdxl_clip.SDXLTokenizer)
else:
is_sdxl_model = False
if is_sdxl_preset != is_sdxl_model:
server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 1, "label": "IPADAPTER (fail)"})
server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 2, "label": "CLIP_VISION (fail)"})
server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 3, "label": "INSIGHTFACE (fail)"})
server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 4, "label": "MODEL (fail)"})
server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 5, "label": "CLIP (fail)"})
logging.error("[Inspire Pack] IPAdapterModelHelper: You cannot mix SDXL and SD1.5 in the checkpoint and IPAdapter.")
raise Exception("[ERROR] You cannot mix SDXL and SD1.5 in the checkpoint and IPAdapter.")
ipadapter, clipvision, lora, is_insightface = model_preset[preset]
ipadapter, ok1 = lookup_model("ipadapter", ipadapter)
clipvision, ok2 = lookup_model("clip_vision", clipvision)
lora, ok3 = lookup_model("loras", lora)
if ok1 == "OK":
ok1 = "IPADAPTER"
else:
ok1 = f"IPADAPTER ({ok1})"
if ok2 == "OK":
ok2 = "CLIP_VISION"
else:
ok2 = f"CLIP_VISION ({ok2})"
server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 1, "label": ok1})
server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 2, "label": ok2})
if ok3 == "FAIL":
server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 4, "label": "MODEL (fail)"})
server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 5, "label": "CLIP (fail)"})
else:
server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 4, "label": "MODEL"})
server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 5, "label": "CLIP"})
if ok1 == "FAIL" or ok2 == "FAIL" or ok3 == "FAIL":
raise Exception("ERROR: Failed to load several models in IPAdapterModelHelper.")
if ipadapter is not None:
ipadapter = nodes.NODE_CLASS_MAPPINGS["IPAdapterModelLoader"]().load_ipadapter_model(ipadapter_file=ipadapter)[0]
ccache_key = ""
if clipvision is not None:
if cache_mode in ["clip_vision only", "all"]:
ccache_key = clipvision
if ccache_key not in backend_support.cache:
backend_support.update_cache(ccache_key, "clipvision", (False, nodes.CLIPVisionLoader().load_clip(clip_name=clipvision)[0]))
_, (_, clipvision) = backend_support.cache[ccache_key]
else:
clipvision = nodes.CLIPVisionLoader().load_clip(clip_name=clipvision)[0]
if lora is not None:
model, clip = nodes.LoraLoader().load_lora(model=model, clip=clip, lora_name=lora, strength_model=lora_strength_model, strength_clip=lora_strength_clip)
def f(x):
return nodes.LoraLoader().load_lora(model=x, clip=clip, lora_name=lora, strength_model=lora_strength_model, strength_clip=lora_strength_clip)
lora_loader = f
else:
def f(x):
return x
lora_loader = f
if 'IPAdapterInsightFaceLoader' in nodes.NODE_CLASS_MAPPINGS:
insight_face_loader = nodes.NODE_CLASS_MAPPINGS['IPAdapterInsightFaceLoader']().load_insightface
else:
logging.warning("'ComfyUI IPAdapter Plus' extension is either too outdated or not installed.")
insight_face_loader = None
icache_key = ""
if is_insightface:
if insight_face_loader is None:
raise Exception("[ERROR] 'ComfyUI IPAdapter Plus' extension is either too outdated or not installed.")
if cache_mode in ["insightface only", "all"]:
icache_key = 'insightface-' + insightface_provider
if icache_key not in backend_support.cache:
backend_support.update_cache(icache_key, "insightface", (False, insight_face_loader(provider=insightface_provider, model_name=insightface_model_name)[0]))
_, (_, insightface) = backend_support.cache[icache_key]
else:
insightface = insight_face_loader(insightface_provider)[0]
server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 3, "label": "INSIGHTFACE"})
else:
insightface = None
server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 3, "label": "INSIGHTFACE (N/A)"})
pipe = ipadapter, model, clipvision, insightface, lora_loader
return pipe, ipadapter, clipvision, insightface, model, clip, icache_key, ccache_key
NODE_CLASS_MAPPINGS = {
"IPAdapterModelHelper //Inspire": IPAdapterModelHelper,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"IPAdapterModelHelper //Inspire": "IPAdapter Model Helper (Inspire)",
}

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import traceback
import comfy
import nodes
import torch
import re
import webcolors
from . import prompt_support
from .libs import utils, common
import logging
class RegionalPromptSimple:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"basic_pipe": ("BASIC_PIPE",),
"mask": ("MASK",),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
"scheduler": (common.get_schedulers(),),
"wildcard_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "placeholder": "wildcard prompt"}),
"controlnet_in_pipe": ("BOOLEAN", {"default": False, "label_on": "Keep", "label_off": "Override"}),
"sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
},
"optional": {
"variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"variation_method": (["linear", "slerp"],),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
RETURN_TYPES = ("REGIONAL_PROMPTS", )
FUNCTION = "doit"
CATEGORY = "InspirePack/Regional"
@staticmethod
def doit(basic_pipe, mask, cfg, sampler_name, scheduler, wildcard_prompt,
controlnet_in_pipe=False, sigma_factor=1.0, variation_seed=0, variation_strength=0.0, variation_method='linear', scheduler_func_opt=None):
if 'RegionalPrompt' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack',
"To use 'RegionalPromptSimple' node, 'Impact Pack' extension is required.")
raise Exception("[ERROR] To use RegionalPromptSimple, you need to install 'ComfyUI-Impact-Pack'")
model, clip, vae, positive, negative = basic_pipe
iwe = nodes.NODE_CLASS_MAPPINGS['ImpactWildcardEncode']()
kap = nodes.NODE_CLASS_MAPPINGS['KSamplerAdvancedProvider']()
rp = nodes.NODE_CLASS_MAPPINGS['RegionalPrompt']()
if wildcard_prompt != "":
model, clip, new_positive, _ = iwe.doit(model=model, clip=clip, populated_text=wildcard_prompt, seed=None)
if controlnet_in_pipe:
prev_cnet = None
for t in positive:
if 'control' in t[1] and 'control_apply_to_uncond' in t[1]:
prev_cnet = t[1]['control'], t[1]['control_apply_to_uncond']
break
if prev_cnet is not None:
for t in new_positive:
t[1]['control'] = prev_cnet[0]
t[1]['control_apply_to_uncond'] = prev_cnet[1]
else:
new_positive = positive
basic_pipe = model, clip, vae, new_positive, negative
sampler = kap.doit(cfg, sampler_name, scheduler, basic_pipe, sigma_factor=sigma_factor, scheduler_func_opt=scheduler_func_opt)[0]
try:
regional_prompts = rp.doit(mask, sampler, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method)[0]
except:
raise Exception("[Inspire-Pack] ERROR: Impact Pack is outdated. Update Impact Pack to latest version to use this.")
return (regional_prompts, )
def color_to_mask(color_mask, mask_color):
try:
if mask_color.startswith("#") or mask_color.isalpha():
hex = mask_color[1:] if mask_color.startswith("#") else webcolors.name_to_hex(mask_color)[1:]
selected = int(hex, 16)
else:
selected = int(mask_color, 10)
except Exception:
raise Exception("[ERROR] Invalid mask_color value. mask_color should be a color value for RGB")
temp = (torch.clamp(color_mask, 0, 1.0) * 255.0).round().to(torch.int)
temp = torch.bitwise_left_shift(temp[:, :, :, 0], 16) + torch.bitwise_left_shift(temp[:, :, :, 1], 8) + temp[:, :, :, 2]
mask = torch.where(temp == selected, 1.0, 0.0)
return mask
class RegionalPromptColorMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"basic_pipe": ("BASIC_PIPE",),
"color_mask": ("IMAGE",),
"mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
"scheduler": (common.get_schedulers(),),
"wildcard_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "placeholder": "wildcard prompt"}),
"controlnet_in_pipe": ("BOOLEAN", {"default": False, "label_on": "Keep", "label_off": "Override"}),
"sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
},
"optional": {
"variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"variation_method": (["linear", "slerp"],),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
RETURN_TYPES = ("REGIONAL_PROMPTS", "MASK")
FUNCTION = "doit"
CATEGORY = "InspirePack/Regional"
@staticmethod
def doit(basic_pipe, color_mask, mask_color, cfg, sampler_name, scheduler, wildcard_prompt,
controlnet_in_pipe=False, sigma_factor=1.0, variation_seed=0, variation_strength=0.0, variation_method="linear", scheduler_func_opt=None):
mask = color_to_mask(color_mask, mask_color)
rp = RegionalPromptSimple().doit(basic_pipe, mask, cfg, sampler_name, scheduler, wildcard_prompt, controlnet_in_pipe,
sigma_factor=sigma_factor, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method, scheduler_func_opt=scheduler_func_opt)[0]
return rp, mask
class RegionalConditioningSimple:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"clip": ("CLIP", ),
"mask": ("MASK",),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"set_cond_area": (["default", "mask bounds"],),
"prompt": ("STRING", {"multiline": True, "placeholder": "prompt"}),
},
}
RETURN_TYPES = ("CONDITIONING", )
FUNCTION = "doit"
CATEGORY = "InspirePack/Regional"
@staticmethod
def doit(clip, mask, strength, set_cond_area, prompt):
conditioning = nodes.CLIPTextEncode().encode(clip, prompt)[0]
conditioning = nodes.ConditioningSetMask().append(conditioning, mask, set_cond_area, strength)[0]
return (conditioning, )
class RegionalConditioningColorMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"clip": ("CLIP", ),
"color_mask": ("IMAGE",),
"mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"set_cond_area": (["default", "mask bounds"],),
"prompt": ("STRING", {"multiline": True, "placeholder": "prompt"}),
},
"optional": {
"dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
}
}
RETURN_TYPES = ("CONDITIONING", "MASK")
FUNCTION = "doit"
CATEGORY = "InspirePack/Regional"
@staticmethod
def doit(clip, color_mask, mask_color, strength, set_cond_area, prompt, dilation=0):
mask = color_to_mask(color_mask, mask_color)
if dilation != 0:
mask = utils.dilate_mask(mask, dilation)
conditioning = nodes.CLIPTextEncode().encode(clip, prompt)[0]
conditioning = nodes.ConditioningSetMask().append(conditioning, mask, set_cond_area, strength)[0]
return conditioning, mask
class ToIPAdapterPipe:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"ipadapter": ("IPADAPTER", ),
"model": ("MODEL",),
},
"optional": {
"clip_vision": ("CLIP_VISION",),
"insightface": ("INSIGHTFACE",),
}
}
RETURN_TYPES = ("IPADAPTER_PIPE",)
FUNCTION = "doit"
CATEGORY = "InspirePack/Util"
@staticmethod
def doit(ipadapter, model, clip_vision, insightface=None):
pipe = ipadapter, model, clip_vision, insightface, lambda x: x
return (pipe,)
class FromIPAdapterPipe:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"ipadapter_pipe": ("IPADAPTER_PIPE", ),
}
}
RETURN_TYPES = ("IPADAPTER", "MODEL", "CLIP_VISION", "INSIGHTFACE")
RETURN_NAMES = ("ipadapter", "model", "clip_vision", "insight_face")
FUNCTION = "doit"
CATEGORY = "InspirePack/Util"
def doit(self, ipadapter_pipe):
ipadapter, model, clip_vision, insightface, _ = ipadapter_pipe
return ipadapter, model, clip_vision, insightface
class IPAdapterConditioning:
def __init__(self, mask, weight, weight_type, noise=None, image=None, neg_image=None, embeds=None, start_at=0.0, end_at=1.0, combine_embeds='concat', unfold_batch=False, weight_v2=False, neg_embeds=None):
self.mask = mask
self.image = image
self.neg_image = neg_image
self.embeds = embeds
self.neg_embeds = neg_embeds
self.weight = weight
self.noise = noise
self.weight_type = weight_type
self.start_at = start_at
self.end_at = end_at
self.unfold_batch = unfold_batch
self.weight_v2 = weight_v2
self.combine_embeds = combine_embeds
def doit(self, ipadapter_pipe):
ipadapter, model, clip_vision, insightface, _ = ipadapter_pipe
if 'IPAdapterAdvanced' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/cubiq/ComfyUI_IPAdapter_plus',
"To use 'Regional IPAdapter' node, 'ComfyUI IPAdapter Plus' extension is required.")
raise Exception("[ERROR] To use IPAdapterModelHelper, you need to install 'ComfyUI IPAdapter Plus'")
if self.embeds is None:
obj = nodes.NODE_CLASS_MAPPINGS['IPAdapterAdvanced']
model = obj().apply_ipadapter(model=model, ipadapter=ipadapter, weight=self.weight, weight_type=self.weight_type,
start_at=self.start_at, end_at=self.end_at, combine_embeds=self.combine_embeds,
clip_vision=clip_vision, image=self.image, image_negative=self.neg_image, attn_mask=self.mask,
insightface=insightface, weight_faceidv2=self.weight_v2)[0]
else:
obj = nodes.NODE_CLASS_MAPPINGS['IPAdapterEmbeds']
model = obj().apply_ipadapter(model=model, ipadapter=ipadapter, pos_embed=self.embeds, weight=self.weight, weight_type=self.weight_type,
start_at=self.start_at, end_at=self.end_at, neg_embed=self.neg_embeds,
attn_mask=self.mask, clip_vision=clip_vision)[0]
return model
IPADAPTER_WEIGHT_TYPES_CACHE = None
def IPADAPTER_WEIGHT_TYPES():
global IPADAPTER_WEIGHT_TYPES_CACHE
if IPADAPTER_WEIGHT_TYPES_CACHE is None:
try:
IPADAPTER_WEIGHT_TYPES_CACHE = nodes.NODE_CLASS_MAPPINGS['IPAdapterAdvanced']().INPUT_TYPES()['required']['weight_type'][0]
except Exception:
logging.error("[Inspire Pack] IPAdapterPlus is not installed.")
IPADAPTER_WEIGHT_TYPES_CACHE = ["IPAdapterPlus is not installed"]
return IPADAPTER_WEIGHT_TYPES_CACHE
class RegionalIPAdapterMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("MASK",),
"image": ("IMAGE",),
"weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}),
"noise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"weight_type": (IPADAPTER_WEIGHT_TYPES(), ),
"start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"unfold_batch": ("BOOLEAN", {"default": False}),
},
"optional": {
"faceid_v2": ("BOOLEAN", {"default": False}),
"weight_v2": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}),
"combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
"neg_image": ("IMAGE",),
}
}
RETURN_TYPES = ("REGIONAL_IPADAPTER", )
FUNCTION = "doit"
CATEGORY = "InspirePack/Regional"
@staticmethod
def doit(mask, image, weight, noise, weight_type, start_at=0.0, end_at=1.0, unfold_batch=False, faceid_v2=False, weight_v2=False, combine_embeds="concat", neg_image=None):
cond = IPAdapterConditioning(mask, weight, weight_type, noise=noise, image=image, neg_image=neg_image, start_at=start_at, end_at=end_at, unfold_batch=unfold_batch, weight_v2=weight_v2, combine_embeds=combine_embeds)
return (cond, )
class RegionalIPAdapterColorMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"color_mask": ("IMAGE",),
"mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}),
"image": ("IMAGE",),
"weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}),
"noise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"weight_type": (IPADAPTER_WEIGHT_TYPES(), ),
"start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"unfold_batch": ("BOOLEAN", {"default": False}),
},
"optional": {
"faceid_v2": ("BOOLEAN", {"default": False }),
"weight_v2": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}),
"combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
"neg_image": ("IMAGE",),
}
}
RETURN_TYPES = ("REGIONAL_IPADAPTER", "MASK")
FUNCTION = "doit"
CATEGORY = "InspirePack/Regional"
@staticmethod
def doit(color_mask, mask_color, image, weight, noise, weight_type, start_at=0.0, end_at=1.0, unfold_batch=False, faceid_v2=False, weight_v2=False, combine_embeds="concat", neg_image=None):
mask = color_to_mask(color_mask, mask_color)
cond = IPAdapterConditioning(mask, weight, weight_type, noise=noise, image=image, neg_image=neg_image, start_at=start_at, end_at=end_at, unfold_batch=unfold_batch, weight_v2=weight_v2, combine_embeds=combine_embeds)
return (cond, mask)
class RegionalIPAdapterEncodedMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("MASK",),
"embeds": ("EMBEDS",),
"weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}),
"weight_type": (IPADAPTER_WEIGHT_TYPES(), ),
"start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"unfold_batch": ("BOOLEAN", {"default": False}),
},
"optional": {
"neg_embeds": ("EMBEDS",),
}
}
RETURN_TYPES = ("REGIONAL_IPADAPTER", )
FUNCTION = "doit"
CATEGORY = "InspirePack/Regional"
@staticmethod
def doit(mask, embeds, weight, weight_type, start_at=0.0, end_at=1.0, unfold_batch=False, neg_embeds=None):
cond = IPAdapterConditioning(mask, weight, weight_type, embeds=embeds, start_at=start_at, end_at=end_at, unfold_batch=unfold_batch, neg_embeds=neg_embeds)
return (cond, )
class RegionalIPAdapterEncodedColorMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"color_mask": ("IMAGE",),
"mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}),
"embeds": ("EMBEDS",),
"weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}),
"weight_type": (IPADAPTER_WEIGHT_TYPES(), ),
"start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"unfold_batch": ("BOOLEAN", {"default": False}),
},
"optional": {
"neg_embeds": ("EMBEDS",),
}
}
RETURN_TYPES = ("REGIONAL_IPADAPTER", "MASK")
FUNCTION = "doit"
CATEGORY = "InspirePack/Regional"
@staticmethod
def doit(color_mask, mask_color, embeds, weight, weight_type, start_at=0.0, end_at=1.0, unfold_batch=False, neg_embeds=None):
mask = color_to_mask(color_mask, mask_color)
cond = IPAdapterConditioning(mask, weight, weight_type, embeds=embeds, start_at=start_at, end_at=end_at, unfold_batch=unfold_batch, neg_embeds=neg_embeds)
return (cond, mask)
class ApplyRegionalIPAdapters:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"ipadapter_pipe": ("IPADAPTER_PIPE",),
"regional_ipadapter1": ("REGIONAL_IPADAPTER", ),
},
}
RETURN_TYPES = ("MODEL", )
FUNCTION = "doit"
CATEGORY = "InspirePack/Regional"
@staticmethod
def doit(**kwargs):
ipadapter_pipe = kwargs['ipadapter_pipe']
ipadapter, model, clip_vision, insightface, lora_loader = ipadapter_pipe
del kwargs['ipadapter_pipe']
for k, v in kwargs.items():
ipadapter_pipe = ipadapter, model, clip_vision, insightface, lora_loader
model = v.doit(ipadapter_pipe)
return (model, )
class RegionalSeedExplorerMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("MASK",),
"noise": ("NOISE_IMAGE",),
"seed_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "pysssss.autocomplete": False}),
"enable_additional": ("BOOLEAN", {"default": True, "label_on": "true", "label_off": "false"}),
"additional_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"additional_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"noise_mode": (["GPU(=A1111)", "CPU"],),
},
"optional":
{"variation_method": (["linear", "slerp"],), }
}
RETURN_TYPES = ("NOISE_IMAGE",)
FUNCTION = "doit"
CATEGORY = "InspirePack/Regional"
@staticmethod
def doit(mask, noise, seed_prompt, enable_additional, additional_seed, additional_strength, noise_mode, variation_method='linear'):
device = comfy.model_management.get_torch_device()
noise_device = "cpu" if noise_mode == "CPU" else device
noise = noise.to(device)
mask = mask.to(device)
if len(mask.shape) == 2:
mask = mask.unsqueeze(0)
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(noise.shape[2], noise.shape[3]), mode="bilinear").squeeze(0)
try:
seed_prompt = seed_prompt.replace("\n", "")
items = seed_prompt.strip().split(",")
if items == ['']:
items = []
if enable_additional:
items.append((additional_seed, additional_strength))
noise = prompt_support.SeedExplorer.apply_variation(noise, items, noise_device, mask, variation_method=variation_method)
except Exception:
logging.error("[Inspire Pack] IGNORED: RegionalSeedExplorerColorMask is failed.")
traceback.print_exc()
noise = noise.cpu()
mask.cpu()
return (noise,)
class RegionalSeedExplorerColorMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"color_mask": ("IMAGE",),
"mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}),
"noise": ("NOISE_IMAGE",),
"seed_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "pysssss.autocomplete": False}),
"enable_additional": ("BOOLEAN", {"default": True, "label_on": "true", "label_off": "false"}),
"additional_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"additional_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"noise_mode": (["GPU(=A1111)", "CPU"],),
},
"optional":
{"variation_method": (["linear", "slerp"],), }
}
RETURN_TYPES = ("NOISE_IMAGE", "MASK")
FUNCTION = "doit"
CATEGORY = "InspirePack/Regional"
@staticmethod
def doit(color_mask, mask_color, noise, seed_prompt, enable_additional, additional_seed, additional_strength, noise_mode, variation_method='linear'):
device = comfy.model_management.get_torch_device()
noise_device = "cpu" if noise_mode == "CPU" else device
color_mask = color_mask.to(device)
noise = noise.to(device)
mask = color_to_mask(color_mask, mask_color)
original_mask = mask
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(noise.shape[2], noise.shape[3]), mode="bilinear").squeeze(0)
mask = mask.to(device)
try:
seed_prompt = seed_prompt.replace("\n", "")
items = seed_prompt.strip().split(",")
if items == ['']:
items = []
if enable_additional:
items.append((additional_seed, additional_strength))
noise = prompt_support.SeedExplorer.apply_variation(noise, items, noise_device, mask, variation_method=variation_method)
except Exception:
logging.error("[Inspire Pack] IGNORED: RegionalSeedExplorerColorMask is failed.")
traceback.print_exc()
color_mask.cpu()
noise = noise.cpu()
original_mask = original_mask.cpu()
return (noise, original_mask)
class ColorMaskToDepthMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"color_mask": ("IMAGE",),
"spec": ("STRING", {"multiline": True, "default": "#FF0000:1.0\n#000000:1.0"}),
"base_value": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0}),
"dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
"flatten_method": (["override", "sum", "max"],),
},
}
RETURN_TYPES = ("MASK", )
FUNCTION = "doit"
CATEGORY = "InspirePack/Regional"
def doit(self, color_mask, spec, base_value, dilation, flatten_method):
specs = spec.split('\n')
pat = re.compile("(?P<color_code>#[A-F0-9]+):(?P<cfg>[0-9]+(.[0-9]*)?)")
masks = [torch.ones((1, color_mask.shape[1], color_mask.shape[2])) * base_value]
for x in specs:
match = pat.match(x)
if match:
mask = color_to_mask(color_mask=color_mask, mask_color=match['color_code']) * float(match['cfg'])
mask = utils.dilate_mask(mask, dilation)
masks.append(mask)
if masks:
masks = torch.cat(masks, dim=0)
if flatten_method == 'override':
masks = utils.flatten_non_zero_override(masks)
elif flatten_method == 'max':
masks = torch.max(masks, dim=0)[0]
else: # flatten_method == 'sum':
masks = torch.sum(masks, dim=0)
masks = torch.clamp(masks, min=0.0, max=1.0)
masks = masks.unsqueeze(0)
else:
masks = torch.tensor([])
return (masks, )
class RegionalCFG:
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL",),
"mask": ("MASK",),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "doit"
CATEGORY = "InspirePack/Regional"
@staticmethod
def doit(model, mask):
if len(mask.shape) == 2:
mask = mask.unsqueeze(0).unsqueeze(0)
elif len(mask.shape) == 3:
mask = mask.unsqueeze(0)
size = None
def regional_cfg(args):
nonlocal mask
nonlocal size
x = args['input']
if mask.device != x.device:
mask = mask.to(x.device)
if size != (x.shape[2], x.shape[3]):
size = (x.shape[2], x.shape[3])
mask = torch.nn.functional.interpolate(mask, size=size, mode='bilinear', align_corners=False)
cond_pred = args["cond_denoised"]
uncond_pred = args["uncond_denoised"]
cond_scale = args["cond_scale"]
cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale * mask
return x - cfg_result
m = model.clone()
m.set_model_sampler_cfg_function(regional_cfg)
return (m,)
NODE_CLASS_MAPPINGS = {
"RegionalPromptSimple //Inspire": RegionalPromptSimple,
"RegionalPromptColorMask //Inspire": RegionalPromptColorMask,
"RegionalConditioningSimple //Inspire": RegionalConditioningSimple,
"RegionalConditioningColorMask //Inspire": RegionalConditioningColorMask,
"RegionalIPAdapterMask //Inspire": RegionalIPAdapterMask,
"RegionalIPAdapterColorMask //Inspire": RegionalIPAdapterColorMask,
"RegionalIPAdapterEncodedMask //Inspire": RegionalIPAdapterEncodedMask,
"RegionalIPAdapterEncodedColorMask //Inspire": RegionalIPAdapterEncodedColorMask,
"RegionalSeedExplorerMask //Inspire": RegionalSeedExplorerMask,
"RegionalSeedExplorerColorMask //Inspire": RegionalSeedExplorerColorMask,
"ToIPAdapterPipe //Inspire": ToIPAdapterPipe,
"FromIPAdapterPipe //Inspire": FromIPAdapterPipe,
"ApplyRegionalIPAdapters //Inspire": ApplyRegionalIPAdapters,
"RegionalCFG //Inspire": RegionalCFG,
"ColorMaskToDepthMask //Inspire": ColorMaskToDepthMask,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"RegionalPromptSimple //Inspire": "Regional Prompt Simple (Inspire)",
"RegionalPromptColorMask //Inspire": "Regional Prompt By Color Mask (Inspire)",
"RegionalConditioningSimple //Inspire": "Regional Conditioning Simple (Inspire)",
"RegionalConditioningColorMask //Inspire": "Regional Conditioning By Color Mask (Inspire)",
"RegionalIPAdapterMask //Inspire": "Regional IPAdapter Mask (Inspire)",
"RegionalIPAdapterColorMask //Inspire": "Regional IPAdapter By Color Mask (Inspire)",
"RegionalIPAdapterEncodedMask //Inspire": "Regional IPAdapter Encoded Mask (Inspire)",
"RegionalIPAdapterEncodedColorMask //Inspire": "Regional IPAdapter Encoded By Color Mask (Inspire)",
"RegionalSeedExplorerMask //Inspire": "Regional Seed Explorer By Mask (Inspire)",
"RegionalSeedExplorerColorMask //Inspire": "Regional Seed Explorer By Color Mask (Inspire)",
"ToIPAdapterPipe //Inspire": "ToIPAdapterPipe (Inspire)",
"FromIPAdapterPipe //Inspire": "FromIPAdapterPipe (Inspire)",
"ApplyRegionalIPAdapters //Inspire": "Apply Regional IPAdapters (Inspire)",
"RegionalCFG //Inspire": "Regional CFG (Inspire)",
"ColorMaskToDepthMask //Inspire": "Color Mask To Depth Mask (Inspire)",
}

353
custom_nodes/comfyui-inspire-pack/inspire/sampler_nodes.py Normal file
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import torch
from . import a1111_compat
import comfy
from .libs import common
from comfy.samplers import CFGGuider
from comfy_extras.nodes_perpneg import Guider_PerpNeg
import math
class KSampler_progress(a1111_compat.KSampler_inspire):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"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": (common.get_schedulers(), ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"noise_mode": (a1111_compat.supported_noise_modes,),
"interval": ("INT", {"default": 1, "min": 1, "max": 10000}),
"omit_start_latent": ("BOOLEAN", {"default": True, "label_on": "True", "label_off": "False"}),
"omit_final_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
},
"optional": {
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
CATEGORY = "InspirePack/analysis"
RETURN_TYPES = ("LATENT", "LATENT")
RETURN_NAMES = ("latent", "progress_latent")
@staticmethod
def doit(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode,
interval, omit_start_latent, omit_final_latent, scheduler_func_opt=None):
adv_steps = int(steps / denoise)
if omit_start_latent:
result = []
else:
result = [comfy.sample.fix_empty_latent_channels(model, latent_image['samples']).cpu()]
def progress_callback(step, x0, x, total_steps):
if (total_steps-1) != step and step % interval != 0:
return
x = model.model.process_latent_out(x)
x = x.cpu()
result.append(x)
latent_image, noise = a1111_compat.KSamplerAdvanced_inspire.sample(model, True, seed, adv_steps, cfg, sampler_name, scheduler, positive, negative, latent_image, (adv_steps-steps),
adv_steps, noise_mode, False, callback=progress_callback, scheduler_func_opt=scheduler_func_opt)
if not omit_final_latent:
result.append(latent_image['samples'].cpu())
if len(result) > 0:
result = torch.cat(result)
result = {'samples': result}
else:
result = latent_image
return latent_image, result
class KSamplerAdvanced_progress(a1111_compat.KSamplerAdvanced_inspire):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (common.get_schedulers(), ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
"noise_mode": (a1111_compat.supported_noise_modes,),
"return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
"interval": ("INT", {"default": 1, "min": 1, "max": 10000}),
"omit_start_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
"omit_final_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
},
"optional": {
"prev_progress_latent_opt": ("LATENT",),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
FUNCTION = "doit"
CATEGORY = "InspirePack/analysis"
RETURN_TYPES = ("LATENT", "LATENT")
RETURN_NAMES = ("latent", "progress_latent")
def doit(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
start_at_step, end_at_step, noise_mode, return_with_leftover_noise, interval, omit_start_latent, omit_final_latent,
prev_progress_latent_opt=None, scheduler_func_opt=None):
if omit_start_latent:
result = []
else:
result = [latent_image['samples']]
def progress_callback(step, x0, x, total_steps):
if (total_steps-1) != step and step % interval != 0:
return
x = model.model.process_latent_out(x)
x = x.cpu()
result.append(x)
latent_image, noise = a1111_compat.KSamplerAdvanced_inspire.sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step,
noise_mode, return_with_leftover_noise, callback=progress_callback, scheduler_func_opt=scheduler_func_opt)
if not omit_final_latent:
result.append(latent_image['samples'].cpu())
if len(result) > 0:
result = torch.cat(result)
result = {'samples': result}
else:
result = latent_image
if prev_progress_latent_opt is not None:
result['samples'] = torch.cat((prev_progress_latent_opt['samples'], result['samples']), dim=0)
return latent_image, result
def exponential_interpolation(from_cfg, to_cfg, i, steps):
if i == steps-1:
return to_cfg
if from_cfg == to_cfg:
return from_cfg
if from_cfg == 0:
return to_cfg * (1 - math.exp(-5 * i / steps)) / (1 - math.exp(-5))
elif to_cfg == 0:
return from_cfg * (math.exp(-5 * i / steps) - math.exp(-5)) / (1 - math.exp(-5))
else:
log_from = math.log(from_cfg)
log_to = math.log(to_cfg)
log_value = log_from + (log_to - log_from) * i / steps
return math.exp(log_value)
def logarithmic_interpolation(from_cfg, to_cfg, i, steps):
if i == 0:
return from_cfg
if i == steps-1:
return to_cfg
log_i = math.log(i + 1)
log_steps = math.log(steps + 1)
t = log_i / log_steps
return from_cfg + (to_cfg - from_cfg) * t
def cosine_interpolation(from_cfg, to_cfg, i, steps):
if (i == 0) or (i == steps-1):
return from_cfg
t = (1.0 + math.cos(math.pi*2*(i/steps))) / 2
return from_cfg + (to_cfg - from_cfg) * t
class Guider_scheduled(CFGGuider):
def __init__(self, model_patcher, sigmas, from_cfg, to_cfg, schedule):
super().__init__(model_patcher)
self.default_cfg = self.cfg
self.sigmas = sigmas
self.cfg_sigmas = None
self.cfg_sigmas_i = None
self.from_cfg = from_cfg
self.to_cfg = to_cfg
self.schedule = schedule
self.last_i = 0
self.renew_cfg_sigmas()
def set_cfg(self, cfg):
self.default_cfg = cfg
self.renew_cfg_sigmas()
def renew_cfg_sigmas(self):
self.cfg_sigmas = {}
self.cfg_sigmas_i = {}
i = 0
steps = len(self.sigmas) - 1
for x in self.sigmas:
k = float(x)
delta = self.to_cfg - self.from_cfg
if self.schedule == 'exp':
self.cfg_sigmas[k] = exponential_interpolation(self.from_cfg, self.to_cfg, i, steps), i
elif self.schedule == 'log':
self.cfg_sigmas[k] = logarithmic_interpolation(self.from_cfg, self.to_cfg, i, steps), i
elif self.schedule == 'cos':
self.cfg_sigmas[k] = cosine_interpolation(self.from_cfg, self.to_cfg, i, steps), i
else:
self.cfg_sigmas[k] = self.from_cfg + delta * i / steps, i
self.cfg_sigmas_i[i] = self.cfg_sigmas[k]
i += 1
def predict_noise(self, x, timestep, model_options={}, seed=None):
k = float(timestep[0])
v = self.cfg_sigmas.get(k)
if v is None:
# fallback
v = self.cfg_sigmas_i[self.last_i+1]
self.cfg_sigmas[k] = v
self.last_i = v[1]
self.cfg = v[0]
return super().predict_noise(x, timestep, model_options, seed)
class Guider_PerpNeg_scheduled(Guider_PerpNeg):
def __init__(self, model_patcher, sigmas, from_cfg, to_cfg, schedule, neg_scale):
super().__init__(model_patcher)
self.default_cfg = self.cfg
self.sigmas = sigmas
self.cfg_sigmas = None
self.cfg_sigmas_i = None
self.from_cfg = from_cfg
self.to_cfg = to_cfg
self.schedule = schedule
self.neg_scale = neg_scale
self.last_i = 0
self.renew_cfg_sigmas()
def set_cfg(self, cfg):
self.default_cfg = cfg
self.renew_cfg_sigmas()
def renew_cfg_sigmas(self):
self.cfg_sigmas = {}
self.cfg_sigmas_i = {}
i = 0
steps = len(self.sigmas) - 1
for x in self.sigmas:
k = float(x)
delta = self.to_cfg - self.from_cfg
if self.schedule == 'exp':
self.cfg_sigmas[k] = exponential_interpolation(self.from_cfg, self.to_cfg, i, steps), i
elif self.schedule == 'log':
self.cfg_sigmas[k] = logarithmic_interpolation(self.from_cfg, self.to_cfg, i, steps), i
elif self.schedule == 'cos':
self.cfg_sigmas[k] = cosine_interpolation(self.from_cfg, self.to_cfg, i, steps), i
else:
self.cfg_sigmas[k] = self.from_cfg + delta * i / steps, i
self.cfg_sigmas_i[i] = self.cfg_sigmas[k]
i += 1
def predict_noise(self, x, timestep, model_options={}, seed=None):
k = float(timestep[0])
v = self.cfg_sigmas.get(k)
if v is None:
# fallback
v = self.cfg_sigmas_i[self.last_i+1]
self.cfg_sigmas[k] = v
self.last_i = v[1]
self.cfg = v[0]
return super().predict_noise(x, timestep, model_options, seed)
class ScheduledCFGGuider:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL", ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"sigmas": ("SIGMAS", ),
"from_cfg": ("FLOAT", {"default": 6.5, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
"to_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
"schedule": (["linear", "log", "exp", "cos"], {'default': 'log'})
}
}
RETURN_TYPES = ("GUIDER", "SIGMAS")
FUNCTION = "get_guider"
CATEGORY = "sampling/custom_sampling/guiders"
def get_guider(self, model, positive, negative, sigmas, from_cfg, to_cfg, schedule):
guider = Guider_scheduled(model, sigmas, from_cfg, to_cfg, schedule)
guider.set_conds(positive, negative)
return guider, sigmas
class ScheduledPerpNegCFGGuider:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL", ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"empty_conditioning": ("CONDITIONING", ),
"neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
"sigmas": ("SIGMAS", ),
"from_cfg": ("FLOAT", {"default": 6.5, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
"to_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
"schedule": (["linear", "log", "exp", "cos"], {'default': 'log'})
}
}
RETURN_TYPES = ("GUIDER", "SIGMAS")
FUNCTION = "get_guider"
CATEGORY = "sampling/custom_sampling/guiders"
def get_guider(self, model, positive, negative, empty_conditioning, neg_scale, sigmas, from_cfg, to_cfg, schedule):
guider = Guider_PerpNeg_scheduled(model, sigmas, from_cfg, to_cfg, schedule, neg_scale)
guider.set_conds(positive, negative, empty_conditioning)
return guider, sigmas
NODE_CLASS_MAPPINGS = {
"KSamplerProgress //Inspire": KSampler_progress,
"KSamplerAdvancedProgress //Inspire": KSamplerAdvanced_progress,
"ScheduledCFGGuider //Inspire": ScheduledCFGGuider,
"ScheduledPerpNegCFGGuider //Inspire": ScheduledPerpNegCFGGuider
}
NODE_DISPLAY_NAME_MAPPINGS = {
"KSamplerProgress //Inspire": "KSampler Progress (Inspire)",
"KSamplerAdvancedProgress //Inspire": "KSampler Advanced Progress (Inspire)",
"ScheduledCFGGuider //Inspire": "Scheduled CFGGuider (Inspire)",
"ScheduledPerpNegCFGGuider //Inspire": "Scheduled PerpNeg CFGGuider (Inspire)"
}

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custom_nodes/comfyui-inspire-pack/inspire/segs_support.py Normal file
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import nodes
import numpy as np
import torch
from .libs import utils
import logging
def normalize_size_base_64(w, h):
short_side = min(w, h)
remainder = short_side % 64
return short_side - remainder + (64 if remainder > 0 else 0)
class MediaPipeFaceMeshDetector:
def __init__(self, face, mouth, left_eyebrow, left_eye, left_pupil, right_eyebrow, right_eye, right_pupil, max_faces, is_segm):
self.face = face
self.mouth = mouth
self.left_eyebrow = left_eyebrow
self.left_eye = left_eye
self.left_pupil = left_pupil
self.right_eyebrow = right_eyebrow
self.right_eye = right_eye
self.right_pupil = right_pupil
self.is_segm = is_segm
self.max_faces = max_faces
self.override_bbox_by_segm = True
def detect(self, image, threshold, dilation, crop_factor, drop_size=1, crop_min_size=None, detailer_hook=None):
if 'MediaPipe-FaceMeshPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'MediaPipeFaceMeshDetector' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use MediaPipeFaceMeshDetector, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
if 'MediaPipeFaceMeshToSEGS' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack',
"To use 'MediaPipeFaceMeshDetector' node, 'Impact Pack' extension is required.")
raise Exception("[ERROR] To use MediaPipeFaceMeshDetector, you need to install 'ComfyUI-Impact-Pack'")
pre_obj = nodes.NODE_CLASS_MAPPINGS['MediaPipe-FaceMeshPreprocessor']
seg_obj = nodes.NODE_CLASS_MAPPINGS['MediaPipeFaceMeshToSEGS']
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
facemesh_image = pre_obj().detect(image, self.max_faces, threshold, resolution=resolution)[0]
facemesh_image = nodes.ImageScale().upscale(facemesh_image, "bilinear", image.shape[2], image.shape[1], "disabled")[0]
segs = seg_obj().doit(facemesh_image, crop_factor, not self.is_segm, crop_min_size, drop_size, dilation,
self.face, self.mouth, self.left_eyebrow, self.left_eye, self.left_pupil,
self.right_eyebrow, self.right_eye, self.right_pupil)[0]
return segs
def setAux(self, x):
pass
class MediaPipe_FaceMesh_Preprocessor_wrapper:
def __init__(self, max_faces, min_confidence, upscale_factor=1.0):
self.max_faces = max_faces
self.min_confidence = min_confidence
self.upscale_factor = upscale_factor
def apply(self, image, mask=None):
if 'MediaPipe-FaceMeshPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
if self.upscale_factor != 1.0:
image = nodes.ImageScaleBy().upscale(image, 'bilinear', self.upscale_factor)[0]
obj = nodes.NODE_CLASS_MAPPINGS['MediaPipe-FaceMeshPreprocessor']()
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
return obj.detect(image, self.max_faces, self.min_confidence, resolution=resolution)[0]
class AnimeLineArt_Preprocessor_wrapper:
def apply(self, image, mask=None):
if 'AnimeLineArtPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'AnimeLineArt_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use AnimeLineArt_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
obj = nodes.NODE_CLASS_MAPPINGS['AnimeLineArtPreprocessor']()
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
return obj.execute(image, resolution=resolution)[0]
class Manga2Anime_LineArt_Preprocessor_wrapper:
def apply(self, image, mask=None):
if 'Manga2Anime_LineArt_Preprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'Manga2Anime_LineArt_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use Manga2Anime_LineArt_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
obj = nodes.NODE_CLASS_MAPPINGS['Manga2Anime_LineArt_Preprocessor']()
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
return obj.execute(image, resolution=resolution)[0]
class Color_Preprocessor_wrapper:
def apply(self, image, mask=None):
if 'ColorPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'Color_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use Color_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
obj = nodes.NODE_CLASS_MAPPINGS['ColorPreprocessor']()
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
return obj.execute(image, resolution=resolution)[0]
class InpaintPreprocessor_wrapper:
def __init__(self, black_pixel_for_xinsir_cn):
self.black_pixel_for_xinsir_cn = black_pixel_for_xinsir_cn
def apply(self, image, mask=None):
if 'InpaintPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'InpaintPreprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use InpaintPreprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
obj = nodes.NODE_CLASS_MAPPINGS['InpaintPreprocessor']()
if mask is None:
mask = torch.ones((image.shape[1], image.shape[2]), dtype=torch.float32, device="cpu").unsqueeze(0)
try:
res = obj.preprocess(image, mask, black_pixel_for_xinsir_cn=self.black_pixel_for_xinsir_cn)[0]
except Exception as e:
if self.black_pixel_for_xinsir_cn:
raise e
else:
res = obj.preprocess(image, mask)[0]
logging.warning("[Inspire Pack] Installed 'ComfyUI's ControlNet Auxiliary Preprocessors.' is outdated.")
return res
class TilePreprocessor_wrapper:
def __init__(self, pyrUp_iters):
self.pyrUp_iters = pyrUp_iters
def apply(self, image, mask=None):
if 'TilePreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'TilePreprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use TilePreprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
obj = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']()
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
return obj.execute(image, self.pyrUp_iters, resolution=resolution)[0]
class MeshGraphormerDepthMapPreprocessorProvider_wrapper:
def apply(self, image, mask=None):
if 'MeshGraphormer-DepthMapPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'MeshGraphormerDepthMapPreprocessorProvider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use MeshGraphormerDepthMapPreprocessorProvider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
obj = nodes.NODE_CLASS_MAPPINGS['MeshGraphormer-DepthMapPreprocessor']()
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
return obj.execute(image, resolution=resolution)[0]
class LineArt_Preprocessor_wrapper:
def __init__(self, coarse):
self.coarse = coarse
def apply(self, image, mask=None):
if 'LineArtPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'LineArt_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use LineArt_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
coarse = 'enable' if self.coarse else 'disable'
obj = nodes.NODE_CLASS_MAPPINGS['LineArtPreprocessor']()
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
return obj.execute(image, resolution=resolution, coarse=coarse)[0]
class OpenPose_Preprocessor_wrapper:
def __init__(self, detect_hand, detect_body, detect_face, upscale_factor=1.0):
self.detect_hand = detect_hand
self.detect_body = detect_body
self.detect_face = detect_face
self.upscale_factor = upscale_factor
def apply(self, image, mask=None):
if 'OpenposePreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'OpenPose_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use OpenPose_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
detect_hand = 'enable' if self.detect_hand else 'disable'
detect_body = 'enable' if self.detect_body else 'disable'
detect_face = 'enable' if self.detect_face else 'disable'
if self.upscale_factor != 1.0:
image = nodes.ImageScaleBy().upscale(image, 'bilinear', self.upscale_factor)[0]
obj = nodes.NODE_CLASS_MAPPINGS['OpenposePreprocessor']()
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
return obj.estimate_pose(image, detect_hand, detect_body, detect_face, resolution=resolution)['result'][0]
class DWPreprocessor_wrapper:
def __init__(self, detect_hand, detect_body, detect_face, upscale_factor=1.0, bbox_detector="yolox_l.onnx", pose_estimator="dw-ll_ucoco_384.onnx"):
self.detect_hand = detect_hand
self.detect_body = detect_body
self.detect_face = detect_face
self.upscale_factor = upscale_factor
self.bbox_detector = bbox_detector
self.pose_estimator = pose_estimator
def apply(self, image, mask=None):
if 'DWPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'DWPreprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use DWPreprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
detect_hand = 'enable' if self.detect_hand else 'disable'
detect_body = 'enable' if self.detect_body else 'disable'
detect_face = 'enable' if self.detect_face else 'disable'
if self.upscale_factor != 1.0:
image = nodes.ImageScaleBy().upscale(image, 'bilinear', self.upscale_factor)[0]
obj = nodes.NODE_CLASS_MAPPINGS['DWPreprocessor']()
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
return obj.estimate_pose(image, detect_hand, detect_body, detect_face, resolution=resolution, bbox_detector=self.bbox_detector, pose_estimator=self.pose_estimator)['result'][0]
class LeReS_DepthMap_Preprocessor_wrapper:
def __init__(self, rm_nearest, rm_background, boost):
self.rm_nearest = rm_nearest
self.rm_background = rm_background
self.boost = boost
def apply(self, image, mask=None):
if 'LeReS-DepthMapPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'LeReS_DepthMap_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use LeReS_DepthMap_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
boost = 'enable' if self.boost else 'disable'
obj = nodes.NODE_CLASS_MAPPINGS['LeReS-DepthMapPreprocessor']()
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
return obj.execute(image, self.rm_nearest, self.rm_background, boost=boost, resolution=resolution)[0]
class MiDaS_DepthMap_Preprocessor_wrapper:
def __init__(self, a, bg_threshold):
self.a = a
self.bg_threshold = bg_threshold
def apply(self, image, mask=None):
if 'MiDaS-DepthMapPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'MiDaS_DepthMap_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use MiDaS_DepthMap_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
obj = nodes.NODE_CLASS_MAPPINGS['MiDaS-DepthMapPreprocessor']()
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
return obj.execute(image, self.a, self.bg_threshold, resolution=resolution)[0]
class Zoe_DepthMap_Preprocessor_wrapper:
def apply(self, image, mask=None):
if 'Zoe-DepthMapPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
"To use 'Zoe_DepthMap_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception("[ERROR] To use Zoe_DepthMap_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
obj = nodes.NODE_CLASS_MAPPINGS['Zoe-DepthMapPreprocessor']()
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
return obj.execute(image, resolution=resolution)[0]
class HED_Preprocessor_wrapper:
def __init__(self, safe, nodename):
self.safe = safe
self.nodename = nodename
def apply(self, image, mask=None):
if self.nodename not in nodes.NODE_CLASS_MAPPINGS:
utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
f"To use '{self.nodename}_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
raise Exception(f"[ERROR] To use {self.nodename}_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
obj = nodes.NODE_CLASS_MAPPINGS[self.nodename]()
resolution = normalize_size_base_64(image.shape[2], image.shape[1])
return obj.execute(image, resolution=resolution, safe="enable" if self.safe else "disable")[0]
class Canny_Preprocessor_wrapper:
def __init__(self, low_threshold, high_threshold):
self.low_threshold = low_threshold
self.high_threshold = high_threshold
def apply(self, image, mask=None):
obj = nodes.NODE_CLASS_MAPPINGS['Canny']()
if hasattr(obj, 'execute'):
# node v3
return obj.execute(image, self.low_threshold, self.high_threshold)[0]
else:
# legacy compatibility
return obj.detect_edge(image, self.low_threshold, self.high_threshold)[0]
class OpenPose_Preprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"detect_hand": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
"detect_body": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
"detect_face": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
"resolution_upscale_by": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 100, "step": 0.1}),
}
}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self, detect_hand, detect_body, detect_face, resolution_upscale_by):
obj = OpenPose_Preprocessor_wrapper(detect_hand, detect_body, detect_face, upscale_factor=resolution_upscale_by)
return (obj, )
class DWPreprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"detect_hand": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
"detect_body": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
"detect_face": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
"resolution_upscale_by": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 100, "step": 0.1}),
"bbox_detector": (
["yolox_l.torchscript.pt", "yolox_l.onnx", "yolo_nas_l_fp16.onnx", "yolo_nas_m_fp16.onnx", "yolo_nas_s_fp16.onnx"],
{"default": "yolox_l.onnx"}
),
"pose_estimator": (["dw-ll_ucoco_384_bs5.torchscript.pt", "dw-ll_ucoco_384.onnx", "dw-ll_ucoco.onnx"], {"default": "dw-ll_ucoco_384_bs5.torchscript.pt"})
}
}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self, detect_hand, detect_body, detect_face, resolution_upscale_by, bbox_detector, pose_estimator):
obj = DWPreprocessor_wrapper(detect_hand, detect_body, detect_face, upscale_factor=resolution_upscale_by, bbox_detector=bbox_detector, pose_estimator=pose_estimator)
return (obj, )
class LeReS_DepthMap_Preprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"rm_nearest": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100, "step": 0.1}),
"rm_background": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100, "step": 0.1})
},
"optional": {
"boost": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
}
}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self, rm_nearest, rm_background, boost=False):
obj = LeReS_DepthMap_Preprocessor_wrapper(rm_nearest, rm_background, boost)
return (obj, )
class MiDaS_DepthMap_Preprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"a": ("FLOAT", {"default": np.pi * 2.0, "min": 0.0, "max": np.pi * 5.0, "step": 0.05}),
"bg_threshold": ("FLOAT", {"default": 0.1, "min": 0, "max": 1, "step": 0.05})
}
}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self, a, bg_threshold):
obj = MiDaS_DepthMap_Preprocessor_wrapper(a, bg_threshold)
return (obj, )
class Zoe_DepthMap_Preprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return { "required": {} }
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self):
obj = Zoe_DepthMap_Preprocessor_wrapper()
return (obj, )
class Canny_Preprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}),
"high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01})
}
}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self, low_threshold, high_threshold):
obj = Canny_Preprocessor_wrapper(low_threshold, high_threshold)
return (obj, )
class HEDPreprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"safe": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"})
}
}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self, safe):
obj = HED_Preprocessor_wrapper(safe, "HEDPreprocessor")
return (obj, )
class FakeScribblePreprocessor_Provider_for_SEGS(HEDPreprocessor_Provider_for_SEGS):
def doit(self, safe):
obj = HED_Preprocessor_wrapper(safe, "FakeScribblePreprocessor")
return (obj, )
class MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"max_faces": ("INT", {"default": 10, "min": 1, "max": 50, "step": 1}),
"min_confidence": ("FLOAT", {"default": 0.5, "min": 0.01, "max": 1.0, "step": 0.01}),
"resolution_upscale_by": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 100, "step": 0.1}),
}
}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self, max_faces, min_confidence, resolution_upscale_by):
obj = MediaPipe_FaceMesh_Preprocessor_wrapper(max_faces, min_confidence, upscale_factor=resolution_upscale_by)
return (obj, )
class MediaPipeFaceMeshDetectorProvider:
@classmethod
def INPUT_TYPES(s):
bool_true_widget = ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"})
bool_false_widget = ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"})
return {"required": {
"max_faces": ("INT", {"default": 10, "min": 1, "max": 50, "step": 1}),
"face": bool_true_widget,
"mouth": bool_false_widget,
"left_eyebrow": bool_false_widget,
"left_eye": bool_false_widget,
"left_pupil": bool_false_widget,
"right_eyebrow": bool_false_widget,
"right_eye": bool_false_widget,
"right_pupil": bool_false_widget,
}}
RETURN_TYPES = ("BBOX_DETECTOR", "SEGM_DETECTOR")
FUNCTION = "doit"
CATEGORY = "InspirePack/Detector"
def doit(self, max_faces, face, mouth, left_eyebrow, left_eye, left_pupil, right_eyebrow, right_eye, right_pupil):
bbox_detector = MediaPipeFaceMeshDetector(face, mouth, left_eyebrow, left_eye, left_pupil, right_eyebrow, right_eye, right_pupil, max_faces, is_segm=False)
segm_detector = MediaPipeFaceMeshDetector(face, mouth, left_eyebrow, left_eye, left_pupil, right_eyebrow, right_eye, right_pupil, max_faces, is_segm=True)
return (bbox_detector, segm_detector)
class AnimeLineArt_Preprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {"required": {}}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self):
obj = AnimeLineArt_Preprocessor_wrapper()
return (obj, )
class Manga2Anime_LineArt_Preprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {"required": {}}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self):
obj = Manga2Anime_LineArt_Preprocessor_wrapper()
return (obj, )
class LineArt_Preprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"coarse": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
}}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self, coarse):
obj = LineArt_Preprocessor_wrapper(coarse)
return (obj, )
class Color_Preprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {"required": {}}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self):
obj = Color_Preprocessor_wrapper()
return (obj, )
class InpaintPreprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {
"required": {},
"optional": {
"black_pixel_for_xinsir_cn": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
}
}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self, black_pixel_for_xinsir_cn=False):
obj = InpaintPreprocessor_wrapper(black_pixel_for_xinsir_cn)
return (obj, )
class TilePreprocessor_Provider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {"required": {'pyrUp_iters': ("INT", {"default": 3, "min": 1, "max": 10, "step": 1})}}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self, pyrUp_iters):
obj = TilePreprocessor_wrapper(pyrUp_iters)
return (obj, )
class MeshGraphormerDepthMapPreprocessorProvider_for_SEGS:
@classmethod
def INPUT_TYPES(s):
return {"required": {}}
RETURN_TYPES = ("SEGS_PREPROCESSOR",)
FUNCTION = "doit"
CATEGORY = "InspirePack/SEGS/ControlNet"
def doit(self):
obj = MeshGraphormerDepthMapPreprocessorProvider_wrapper()
return (obj, )
NODE_CLASS_MAPPINGS = {
"OpenPose_Preprocessor_Provider_for_SEGS //Inspire": OpenPose_Preprocessor_Provider_for_SEGS,
"DWPreprocessor_Provider_for_SEGS //Inspire": DWPreprocessor_Provider_for_SEGS,
"MiDaS_DepthMap_Preprocessor_Provider_for_SEGS //Inspire": MiDaS_DepthMap_Preprocessor_Provider_for_SEGS,
"LeRes_DepthMap_Preprocessor_Provider_for_SEGS //Inspire": LeReS_DepthMap_Preprocessor_Provider_for_SEGS,
# "Zoe_DepthMap_Preprocessor_Provider_for_SEGS //Inspire": Zoe_DepthMap_Preprocessor_Provider_for_SEGS,
"Canny_Preprocessor_Provider_for_SEGS //Inspire": Canny_Preprocessor_Provider_for_SEGS,
"MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS //Inspire": MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS,
"HEDPreprocessor_Provider_for_SEGS //Inspire": HEDPreprocessor_Provider_for_SEGS,
"FakeScribblePreprocessor_Provider_for_SEGS //Inspire": FakeScribblePreprocessor_Provider_for_SEGS,
"AnimeLineArt_Preprocessor_Provider_for_SEGS //Inspire": AnimeLineArt_Preprocessor_Provider_for_SEGS,
"Manga2Anime_LineArt_Preprocessor_Provider_for_SEGS //Inspire": Manga2Anime_LineArt_Preprocessor_Provider_for_SEGS,
"LineArt_Preprocessor_Provider_for_SEGS //Inspire": LineArt_Preprocessor_Provider_for_SEGS,
"Color_Preprocessor_Provider_for_SEGS //Inspire": Color_Preprocessor_Provider_for_SEGS,
"InpaintPreprocessor_Provider_for_SEGS //Inspire": InpaintPreprocessor_Provider_for_SEGS,
"TilePreprocessor_Provider_for_SEGS //Inspire": TilePreprocessor_Provider_for_SEGS,
"MeshGraphormerDepthMapPreprocessorProvider_for_SEGS //Inspire": MeshGraphormerDepthMapPreprocessorProvider_for_SEGS,
"MediaPipeFaceMeshDetectorProvider //Inspire": MediaPipeFaceMeshDetectorProvider,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"OpenPose_Preprocessor_Provider_for_SEGS //Inspire": "OpenPose Preprocessor Provider (SEGS)",
"DWPreprocessor_Provider_for_SEGS //Inspire": "DWPreprocessor Provider (SEGS)",
"MiDaS_DepthMap_Preprocessor_Provider_for_SEGS //Inspire": "MiDaS Depth Map Preprocessor Provider (SEGS)",
"LeRes_DepthMap_Preprocessor_Provider_for_SEGS //Inspire": "LeReS Depth Map Preprocessor Provider (SEGS)",
# "Zoe_DepthMap_Preprocessor_Provider_for_SEGS //Inspire": "Zoe Depth Map Preprocessor Provider (SEGS)",
"Canny_Preprocessor_Provider_for_SEGS //Inspire": "Canny Preprocessor Provider (SEGS)",
"MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS //Inspire": "MediaPipe FaceMesh Preprocessor Provider (SEGS)",
"HEDPreprocessor_Provider_for_SEGS //Inspire": "HED Preprocessor Provider (SEGS)",
"FakeScribblePreprocessor_Provider_for_SEGS //Inspire": "Fake Scribble Preprocessor Provider (SEGS)",
"AnimeLineArt_Preprocessor_Provider_for_SEGS //Inspire": "AnimeLineArt Preprocessor Provider (SEGS)",
"Manga2Anime_LineArt_Preprocessor_Provider_for_SEGS //Inspire": "Manga2Anime LineArt Preprocessor Provider (SEGS)",
"LineArt_Preprocessor_Provider_for_SEGS //Inspire": "LineArt Preprocessor Provider (SEGS)",
"Color_Preprocessor_Provider_for_SEGS //Inspire": "Color Preprocessor Provider (SEGS)",
"InpaintPreprocessor_Provider_for_SEGS //Inspire": "Inpaint Preprocessor Provider (SEGS)",
"TilePreprocessor_Provider_for_SEGS //Inspire": "Tile Preprocessor Provider (SEGS)",
"MeshGraphormerDepthMapPreprocessorProvider_for_SEGS //Inspire": "MeshGraphormer Depth Map Preprocessor Provider (SEGS)",
"MediaPipeFaceMeshDetectorProvider //Inspire": "MediaPipeFaceMesh Detector Provider",
}

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custom_nodes/comfyui-inspire-pack/inspire/util_nodes.py Normal file
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import colorsys
def hex_to_hsv(hex_color):
hex_color = hex_color.lstrip('#')
r, g, b = tuple(int(hex_color[i:i+2], 16) / 255.0 for i in (0, 2, 4))
h, s, v = colorsys.rgb_to_hsv(r, g, b)
hue = h * 360
saturation = s
value = v
return hue, saturation, value
class RGB_HexToHSV:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"rgb_hex": ("STRING", {"defaultInput": True}),
},
}
RETURN_TYPES = ("FLOAT", "FLOAT", "FLOAT")
RETURN_NAMES = ("hue", "saturation", "value")
FUNCTION = "doit"
CATEGORY = "InspirePack/Util"
def doit(self, rgb_hex):
return hex_to_hsv(rgb_hex)
NODE_CLASS_MAPPINGS = {
"RGB_HexToHSV //Inspire": RGB_HexToHSV,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"RGB_HexToHSV //Inspire": "RGB Hex To HSV (Inspire)",
}

27
custom_nodes/comfyui-inspire-pack/js/common.js Normal file
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import { api } from "../../scripts/api.js";
function nodeFeedbackHandler(event) {
let nodes = app.graph._nodes_by_id;
let node = nodes[event.detail.node_id];
if(node) {
if(event.detail.type == "text") {
const w = node.widgets.find((w) => event.detail.widget_name === w.name);
if(w) {
w.value = event.detail.data;
}
}
}
}
api.addEventListener("inspire-node-feedback", nodeFeedbackHandler);
function nodeOutputLabelHandler(event) {
let nodes = app.graph._nodes_by_id;
let node = nodes[event.detail.node_id];
if(node) {
node.outputs[event.detail.output_idx].label = event.detail.label;
}
}
api.addEventListener("inspire-node-output-label", nodeOutputLabelHandler);

71
custom_nodes/comfyui-inspire-pack/js/image_util.js Normal file
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import { ComfyApp, app } from "../../scripts/app.js";
function load_image(str) {
let base64String = canvas.toDataURL('image/png');
let img = new Image();
img.src = base64String;
}
app.registerExtension({
name: "Comfy.Inspire.img",
nodeCreated(node, app) {
if(node.comfyClass == "LoadImage //Inspire") {
let w = node.widgets.find(obj => obj.name === 'image_data');
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 {
return w._value;
}
}
});
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(w.value && w.value != '[IMAGE DATA]')
this._img[0].src = w.value;
}
return this._img;
}
});
}
}
})

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custom_nodes/comfyui-inspire-pack/js/inspire-backend.js Normal file
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import { api } from "../../scripts/api.js";
async function refresh_data(node) {
let response = await api.fetchApi('/inspire/cache/list');
node.widgets[0].value = await response.text();
}
async function remove_key(node, key) {
await api.fetchApi(`/inspire/cache/remove?key=${key}`);
node.widgets[1].value = '';
refresh_data(node);
}
async function clear_data(node) {
await api.fetchApi('/inspire/cache/clear');
refresh_data(node);
}
async function set_cache_settings(node) {
await api.fetchApi('/inspire/cache/settings', {
method: "POST",
headers: {"Content-Type": "application/json",},
body: node.widgets[0].value,
});
refresh_data(node);
}
export function register_cache_info(node, app) {
if(node.comfyClass == "ShowCachedInfo //Inspire") {
node.addWidget("button", "Remove Key", null, () => { remove_key(node, node.widgets[1].value); });
node.addWidget("button", "Save Settings", null, () => { set_cache_settings(node); });
node.addWidget("button", "Refresh", null, () => { refresh_data(node); });
node.addWidget("button", "Clear", null, () => { clear_data(node); });
refresh_data(node);
}
}

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custom_nodes/comfyui-inspire-pack/js/inspire-flex.js Normal file
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import { ComfyApp, app } from "../../scripts/app.js";
export function register_concat_conditionings_with_multiplier_node(nodeType, nodeData, app) {
if (nodeData.name === 'ConcatConditioningsWithMultiplier //Inspire') {
var input_name = "conditioning";
const onConnectionsChange = nodeType.prototype.onConnectionsChange;
let this_handler = async function (type, index, connected, link_info) {
let last_state = this.state_change_handling;
try {
this.state_change_handling = true;
if(!link_info || link_info.type != 'CONDITIONING')
return;
let self = this;
function get_input_count(prefix, linked_only) {
let cnt = 0;
for(let i in self.inputs) {
if(linked_only && !self.inputs[i].link)
continue;
if(self.inputs[i].name.startsWith(prefix))
cnt+=1;
}
return cnt;
}
function get_widget_count(prefix) {
let cnt = 0;
for(let i in self.widgets) {
if(self.widgets[i].name.startsWith(prefix))
cnt+=1;
}
return cnt;
}
function get_unconnected() {
let unconnected = [];
for(let i in self.inputs) {
let input = self.inputs[i];
if(input.name.startsWith('conditioning')) {
if(input.link == undefined)
unconnected.push(i);
}
}
return unconnected;
}
let unconnected = get_unconnected();
function renames() {
let con_i = 1;
let rename_map = {};
for(let i in self.inputs) {
let input = self.inputs[i];
if(input.name.startsWith('conditioning')) {
let orig_i = Number(input.name.substring(12));
if(orig_i != con_i) {
rename_map[orig_i] = con_i;
input.name = 'conditioning'+con_i;
}
con_i++;
}
}
// update multiplier input
for(let i in self.inputs) {
let input = self.inputs[i];
if(input.name.startsWith('multiplier')) {
let orig_i = Number(input.name.substring(10));
if(rename_map[orig_i]) {
input.name = 'multiplier'+rename_map[orig_i];
}
}
}
// update multiplier widget
for(let i in self.widgets) {
let w = self.widgets[i];
if(w.name.startsWith('multiplier')) {
let orig_i = Number(w.name.substring(10));
if(rename_map[orig_i]) {
w.name = 'multiplier'+rename_map[orig_i];
}
}
}
return con_i;
}
function remove_multiplier_link(i, link_id) {
let link = app.graph.links[link_id];
const node = app.graph.getNodeById(link.origin_id);
let x = node.outputs[link.origin_slot].links.findIndex((w) => w == link_id);
node.outputs[link.origin_slot].links.splice(x, 1);
self.disconnectInput(i);
app.graph.links.splice(link_id, 1);
}
async function remove_target_multiplier(target_name) {
// remove strength from slot
for(let i in self.inputs) {
let input = self.inputs[i];
if(input.name.startsWith(target_name)) {
if(input.link) {
remove_multiplier_link(i, input.link);
}
await self.removeInput(i);
break;
}
}
const widget_index = self.widgets.findIndex((w) => w.name == target_name);
self.widgets.splice(widget_index, 1);
}
async function remove_garbage() {
let unconnected = get_unconnected();
// remove unconnected conditionings
while(unconnected.length > 0) {
let last_one = unconnected.reverse()[0];
self.removeInput(last_one);
unconnected = get_unconnected();
}
// remove dangling multipliers
let conds = new Set();
let muls = new Set();
for(let i in self.inputs) {
let input = self.inputs[i];
if(input.link && input.name.startsWith('conditioning')) {
let index = Number(input.name.substring(12));
conds.add(index);
}
else if(input.name.startsWith('multiplier')) {
let index = Number(input.name.substring(10));
muls.add(index);
}
}
for(let i in self.widgets) {
let index = Number(self.widgets[i].name.substring(10));
muls.add(index);
}
let dangling_muls = [...muls].filter(x => !conds.has(x));
while(dangling_muls.length > 0) {
let remove_target = dangling_muls.pop();
let target_name = `multiplier${remove_target}`;
await remove_target_multiplier(target_name);
}
}
async function ensure_multipliers() {
if(self.ensuring_multipliers) {
return;
}
try {
self.ensuring_multipliers = true;
let ncon = get_input_count('conditioning', true);
let nmul = get_input_count('multiplier', false) + get_widget_count('multiplier');
if(ncon == 0 && nmul == 0)
ncon = 1;
for(let i = nmul+1; i<=ncon; i++) {
let config = { min: 0, max: 10, step: 0.1, round: 0.01, precision: 2 };
// NOTE: addWidget trigger calling ensure_multipliers
let widget = await self.addWidget("number", `multiplier${i}`, 1.0, function (v) {
if (config.round) {
self.value = Math.round(v/config.round)*config.round;
} else {
self.value = v;
}
}, config);
}
}
finally{
self.ensuring_multipliers = null;
}
}
async function recover_multipliers() {
if(self.recover_multipliers) {
return;
}
try {
self.recover_multipliers = true;
for(let i = 1; i<self.widgets_values.length; i++) {
let config = { min: 0, max: 10, step: 0.1, round: 0.01, precision: 2 };
// NOTE: addWidget trigger calling recover_multipliers
let widget = await self.addWidget("number", `multiplier${i+1}`, 1.0, function (v) {
if (config.round) {
self.value = Math.round(v/config.round)*config.round;
} else {
self.value = v;
}
}, config);
}
}
finally{
self.recover_multipliers = null;
}
}
async function ensure_inputs() {
if(get_unconnected() == 0) {
let con_i = renames();
self.addInput(`conditioning${con_i}`, self.outputs[0].type);
}
}
const stackTrace = new Error().stack;
if(!stackTrace.includes('loadGraphData') && !stackTrace.includes('pasteFromClipboard')) {
await remove_garbage();
await ensure_inputs();
}
if(!stackTrace.includes('loadGraphData')) {
await ensure_multipliers();
}
else {
await recover_multipliers();
}
await this.setSize( this.computeSize() );
}
finally {
this.state_change_handling = last_state;
}
}
nodeType.prototype.onConnectionsChange = this_handler;
}
}
function ensure_splitter_outputs(node, output_name, value, type) {
if(node.outputs.length != (value + 1)) {
while(node.outputs.length != (value + 1)) {
if(node.outputs.length > value + 1) {
node.removeOutput(node.outputs.length-1);
}
else {
node.addOutput(`output${node.outputs.length+1}`, type);
}
}
for(let i in node.outputs) {
let output = node.outputs[i];
output.name = `${output_name} ${parseInt(i)+1}`;
}
if(node.outputs[0].label == type || node.outputs[0].label == 'remained')
delete node.outputs[0].label;
let last_output = node.outputs[node.outputs.length-1];
last_output.name = 'remained';
}
}
export function register_splitter(node, app) {
if(node.comfyClass === 'ImageBatchSplitter //Inspire' || node.comfyClass === 'LatentBatchSplitter //Inspire') {
let split_count = node.widgets[0];
let output_name = 'output';
let output_type = "*";
if(node.comfyClass === 'ImageBatchSplitter //Inspire') {
output_name = 'image';
output_type = "IMAGE";
}
else if(node.comfyClass === 'LatentBatchSplitter //Inspire') {
output_name = 'latent';
output_type = "LATENT";
}
ensure_splitter_outputs(node, output_name, split_count.value, output_type);
Object.defineProperty(split_count, "value", {
set: async function(value) {
if(value < 0 || value > 50)
return;
ensure_splitter_outputs(node, output_name, value, output_type);
},
get: function() {
return node.outputs.length - 1;
}
});
}
}

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custom_nodes/comfyui-inspire-pack/js/inspire-loop.js Normal file
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import { inspireProgressBadge } from "./progress-badge.js"
export function register_loop_node(nodeType, nodeData, app) {
if(nodeData.name == 'ForeachListEnd //Inspire') {
inspireProgressBadge.addStatusHandler(nodeType);
}
}

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custom_nodes/comfyui-inspire-pack/js/inspire-pack.js Normal file
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import { ComfyApp, app } from "../../scripts/app.js";
import { register_concat_conditionings_with_multiplier_node, register_splitter } from "./inspire-flex.js";
import { register_cache_info } from "./inspire-backend.js";
import { register_loop_node } from "./inspire-loop.js";
app.registerExtension({
name: "Comfy.Inspire",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
await register_concat_conditionings_with_multiplier_node(nodeType, nodeData, app);
await register_loop_node(nodeType, nodeData, app);
},
nodeCreated(node, app) {
register_cache_info(node, app);
register_splitter(node, app);
}
})

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custom_nodes/comfyui-inspire-pack/js/lora_block_weight.js Normal file
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import { ComfyApp, app } from "../../scripts/app.js";
app.registerExtension({
name: "Comfy.Inspire.LBW",
nodeCreated(node, app) {
if(node.comfyClass == "LoraLoaderBlockWeight //Inspire" || node.comfyClass == "MakeLBW //Inspire") {
// category filter
const lora_names_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'lora_name')];
var full_lora_list = lora_names_widget.options.values;
const category_filter_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'category_filter')];
Object.defineProperty(lora_names_widget.options, "values", {
set: (x) => {
full_lora_list = x;
},
get: () => {
if(category_filter_widget.value == 'All')
return full_lora_list;
let l = full_lora_list.filter(x => x.startsWith(category_filter_widget.value));
return l;
}
});
// vector selector
let preset_i = 9;
let vector_i = 10;
if(node.comfyClass == "MakeLBW //Inspire") {
preset_i = 7;
vector_i = 8;
}
node._value = "Preset";
node.widgets[preset_i].callback = (v, canvas, node, pos, e) => {
node.widgets[vector_i].value = node._value.split(':')[1];
if(node.widgets_values) {
node.widgets_values[vector_i] = node.widgets[preset_i].value;
}
}
Object.defineProperty(node.widgets[preset_i], "value", {
set: (value) => {
if(value != "Preset")
node._value = value;
},
get: () => {
return node._value;
}
});
}
if(node.comfyClass == "XY Input: Lora Block Weight //Inspire") {
// category filter
const lora_names_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'lora_name')];
var full_lora_list = lora_names_widget.options.values;
const category_filter_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'category_filter')];
Object.defineProperty(lora_names_widget.options, "values", {
set: (x) => {
full_lora_list = x;
},
get: () => {
if(category_filter_widget.value == 'All')
return full_lora_list;
let l = full_lora_list.filter(x => x.startsWith(category_filter_widget.value));
return l;
}
});
// vector selector
let preset_i = 9;
let vector_i = 10;
node._value = "Preset";
node.widgets[preset_i].callback = (v, canvas, node, pos, e) => {
let value = node._value;
if(!value.startsWith('@') && node.widgets[vector_i].value != "")
node.widgets[vector_i].value += "\n";
if(value.startsWith('@')) {
let spec = value.split(':')[1];
var n;
var sub_n = null;
var block = null;
if(isNaN(spec)) {
let sub_spec = spec.split(',');
if(sub_spec.length != 3) {
node.widgets_values[vector_i] = '!! SPEC ERROR !!';
node._value = '';
return;
}
n = parseInt(sub_spec[0].trim());
sub_n = parseInt(sub_spec[1].trim());
block = parseInt(sub_spec[2].trim());
}
else {
n = parseInt(spec.trim());
}
node.widgets[vector_i].value = "";
if(sub_n == null) {
for(let i=1; i<=n; i++) {
var temp = "";
for(let j=1; j<=n; j++) {
if(temp!='')
temp += ',';
if(j==i)
temp += 'A';
else
temp += '0';
}
node.widgets[vector_i].value += `B${i}:${temp}\n`;
}
}
else {
for(let i=1; i<=sub_n; i++) {
var temp = "";
for(let j=1; j<=n; j++) {
if(temp!='')
temp += ',';
if(block!=j)
temp += '0';
else {
temp += ' ';
for(let k=1; k<=sub_n; k++) {
if(k==i)
temp += 'A ';
else
temp += '0 ';
}
}
}
node.widgets[vector_i].value += `B${block}.SUB${i}:${temp}\n`;
}
}
}
else {
node.widgets[vector_i].value += `${value}/${value.split(':')[0]}`;
}
if(node.widgets_values) {
node.widgets_values[vector_i] = node.widgets[preset_i].value;
}
}
Object.defineProperty(node.widgets[preset_i], "value", {
set: (value) => {
if(value != 'Preset')
node._value = value;
},
get: () => {
return node._value;
}
});
}
}
});

76
custom_nodes/comfyui-inspire-pack/js/progress-badge.js Normal file
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import { api } from "../../scripts/api.js";
// copying from https://github.com/pythongosssss/ComfyUI-WD14-Tagger
class InspireProgressBadge {
constructor() {
if (!window.__progress_badge__) {
window.__progress_badge__ = Symbol("__inspire_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("inspire/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;
};
}
}
export const inspireProgressBadge = new InspireProgressBadge();

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custom_nodes/comfyui-inspire-pack/js/prompt.js Normal file
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import { ComfyApp, app } from "../../scripts/app.js";
import { api } from "../../scripts/api.js";
let get_wildcards_list;
let get_wildcard_label;
let is_wildcard_label;
let load_wildcard_status;
try {
const ImpactPack = await import("../ComfyUI-Impact-Pack/impact-pack.js");
console.log("[Inspire Pack] Impact Pack module loaded:", ImpactPack);
get_wildcards_list = ImpactPack.get_wildcards_list;
get_wildcard_label = ImpactPack.get_wildcard_label;
is_wildcard_label = ImpactPack.is_wildcard_label;
load_wildcard_status = ImpactPack.load_wildcard_status;
console.log("[Inspire Pack] Functions imported:", {
get_wildcards_list: !!get_wildcards_list,
get_wildcard_label: !!get_wildcard_label,
is_wildcard_label: !!is_wildcard_label,
load_wildcard_status: !!load_wildcard_status
});
}
catch (error) {
console.error("[Inspire Pack] Failed to import Impact Pack module:", error);
}
// Fallback for get_wildcards_list
if(!get_wildcards_list) {
console.warn("[Inspire Pack] get_wildcards_list not available. Using fallback.");
get_wildcards_list = () => {
return ["Impact Pack isn't installed or needs browser cache refresh."];
}
}
// Fallback for on-demand features (backward compatibility with older Impact Pack)
if(!get_wildcard_label) {
get_wildcard_label = () => { return "Select the Wildcard to add to the text"; };
}
if(!is_wildcard_label) {
is_wildcard_label = (value) => { return value === "Select the Wildcard to add to the text"; };
}
if(!load_wildcard_status) {
load_wildcard_status = async () => {}; // No-op for older versions
}
let pb_cache = {};
async function get_prompt_builder_items(category) {
if(pb_cache[category])
return pb_cache[category];
else {
let res = await api.fetchApi(`/inspire/prompt_builder?category=${category}`);
let data = await res.json();
pb_cache[category] = data.presets;
return data.presets;
}
}
app.registerExtension({
name: "Comfy.Inspire.Prompts",
nodeCreated(node, app) {
if(node.comfyClass == "WildcardEncode //Inspire") {
const wildcard_text_widget_index = node.widgets.findIndex((w) => w.name == 'wildcard_text');
const populated_text_widget_index = node.widgets.findIndex((w) => w.name == 'populated_text');
const mode_widget_index = node.widgets.findIndex((w) => w.name == 'mode');
const wildcard_text_widget = node.widgets[wildcard_text_widget_index];
const populated_text_widget = node.widgets[populated_text_widget_index];
// lora selector, wildcard selector
let combo_id = 5;
// lora
node.widgets[combo_id].callback = (value, canvas, node, pos, e) => {
let lora_name = node._value;
if(lora_name.endsWith('.safetensors')) {
lora_name = lora_name.slice(0, -12);
}
wildcard_text_widget.value += `<lora:${lora_name}>`;
}
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"; }
});
// wildcard
node.widgets[combo_id+1].callback = async (value, canvas, node, pos, e) => {
if(wildcard_text_widget.value != '')
wildcard_text_widget.value += ', '
wildcard_text_widget.value += node._wildcard_value;
// Reload wildcard status to update loaded count (Impact Pack staged feature)
await load_wildcard_status();
app.canvas.setDirty(true);
}
Object.defineProperty(node.widgets[combo_id+1], "value", {
set: (value) => {
if (!is_wildcard_label(value))
node._wildcard_value = value;
},
get: () => { return get_wildcard_label(); }
});
Object.defineProperty(node.widgets[combo_id+1].options, "values", {
set: (x) => {},
get: () => {
return get_wildcards_list();
}
});
// Preventing validation errors from occurring in any situation.
node.widgets[combo_id].serializeValue = () => { return "Select the LoRA to add to the text"; }
node.widgets[combo_id+1].serializeValue = () => {
// Always serialize as the default label (not the dynamic on-demand label)
return "Select the Wildcard to add to the text";
}
// wildcard populating
populated_text_widget.inputEl.disabled = true;
const mode_widget = node.widgets[mode_widget_index];
// 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';
}
});
}
else if(node.comfyClass == "MakeBasicPipe //Inspire") {
const pos_wildcard_text_widget = node.widgets.find((w) => w.name == 'positive_wildcard_text');
const pos_populated_text_widget = node.widgets.find((w) => w.name == 'positive_populated_text');
const neg_wildcard_text_widget = node.widgets.find((w) => w.name == 'negative_wildcard_text');
const neg_populated_text_widget = node.widgets.find((w) => w.name == 'negative_populated_text');
const mode_widget = node.widgets.find((w) => w.name == 'wildcard_mode');
const direction_widget = node.widgets.find((w) => w.name == 'Add selection to');
// lora selector, wildcard selector
let combo_id = 5;
node.widgets[combo_id].callback = (value, canvas, node, pos, e) => {
let lora_name = node._lora_value;
if (lora_name.endsWith('.safetensors')) {
lora_name = lora_name.slice(0, -12);
}
if(direction_widget.value) {
pos_wildcard_text_widget.value += `<lora:${lora_name}>`;
}
else {
neg_wildcard_text_widget.value += `<lora:${lora_name}>`;
}
}
Object.defineProperty(node.widgets[combo_id], "value", {
set: (value) => {
if (value !== "Select the LoRA to add to the text")
node._lora_value = value;
},
get: () => { return "Select the LoRA to add to the text"; }
});
node.widgets[combo_id+1].callback = async (value, canvas, node, pos, e) => {
let w = null;
if(direction_widget.value) {
w = pos_wildcard_text_widget;
}
else {
w = neg_wildcard_text_widget;
}
if(w.value != '')
w.value += ', '
w.value += node._wildcard_value;
// Reload wildcard status to update loaded count (Impact Pack staged feature)
await load_wildcard_status();
app.canvas.setDirty(true);
}
Object.defineProperty(node.widgets[combo_id+1], "value", {
set: (value) => {
if (!is_wildcard_label(value))
node._wildcard_value = value;
},
get: () => { return get_wildcard_label(); }
});
Object.defineProperty(node.widgets[combo_id+1].options, "values", {
set: (x) => {},
get: () => {
return get_wildcards_list();
}
});
// Preventing validation errors from occurring in any situation.
node.widgets[combo_id].serializeValue = () => { return "Select the LoRA to add to the text"; }
node.widgets[combo_id+1].serializeValue = () => {
// Always serialize as the default label (not the dynamic on-demand label)
return "Select the Wildcard to add to the text";
}
// wildcard populating
pos_populated_text_widget.inputEl.disabled = true;
neg_populated_text_widget.inputEl.disabled = true;
// 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
pos_populated_text_widget.inputEl.disabled = node._mode_value == 'populate';
neg_populated_text_widget.inputEl.disabled = node._mode_value == 'populate';
},
get: () => {
if(node._mode_value != undefined)
return node._mode_value;
else
return 'populate';
}
});
}
else if(node.comfyClass == "PromptBuilder //Inspire") {
const preset_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'preset')];
const category_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'category')];
Object.defineProperty(preset_widget.options, "values", {
set: (x) => {},
get: () => {
get_prompt_builder_items(category_widget.value);
if(pb_cache[category_widget.value] == undefined) {
return ["#PRESET"];
}
return pb_cache[category_widget.value];
}
});
preset_widget.callback = (value, canvas, node, pos, e) => {
if(node.widgets[2].value) {
node.widgets[2].value += ', ';
}
const y = node._preset_value.split(':');
if(y.length == 2)
node.widgets[2].value += y[1].trim();
else
node.widgets[2].value += node._preset_value.trim();
}
Object.defineProperty(preset_widget, "value", {
set: (value) => {
if (value !== "#PRESET")
node._preset_value = value;
},
get: () => { return '#PRESET'; }
});
preset_widget.serializeValue = (workflowNode, widgetIndex) => { return "#PRESET"; };
}
else if(node.comfyClass == "SeedExplorer //Inspire"
|| node.comfyClass == "RegionalSeedExplorerMask //Inspire"
|| node.comfyClass == "RegionalSeedExplorerColorMask //Inspire") {
const prompt_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'seed_prompt')];
const seed_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'additional_seed')];
const strength_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'additional_strength')];
let allow_init_seed = node.comfyClass == "SeedExplorer //Inspire";
node.addWidget("button", "Add to prompt", null, () => {
if(!prompt_widget.value?.trim() && allow_init_seed) {
prompt_widget.value = ''+seed_widget.value;
}
else {
if(prompt_widget.value?.trim())
prompt_widget.value += ', ';
prompt_widget.value += `${seed_widget.value}:${strength_widget.value.toFixed(2)}`;
seed_widget.value += 1;
}
});
}
}
});
const original_queuePrompt = api.queuePrompt;
async function queuePrompt_with_widget_idxs(number, { output, workflow }, ...args) {
workflow.widget_idx_map = {};
for(let i in app.graph._nodes_by_id) {
let widgets = app.graph._nodes_by_id[i].widgets;
if(widgets) {
for(let j in widgets) {
if(['seed', 'noise_seed', 'sampler_name', 'scheduler'].includes(widgets[j].name)
&& widgets[j].type != 'converted-widget') {
if(workflow.widget_idx_map[i] == undefined) {
workflow.widget_idx_map[i] = {};
}
workflow.widget_idx_map[i][widgets[j].name] = parseInt(j);
}
}
}
}
return await original_queuePrompt.call(api, number, { output, workflow }, ...args);
}
api.queuePrompt = queuePrompt_with_widget_idxs;

64
custom_nodes/comfyui-inspire-pack/js/regional.js Normal file
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import { ComfyApp, app } from "../../scripts/app.js";
import { ComfyDialog, $el } from "../../scripts/ui.js";
import { api } from "../../scripts/api.js";
app.registerExtension({
name: "Comfy.Inspire.Regional",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (nodeData.name === 'ApplyRegionalIPAdapters //Inspire') {
var input_name = "input";
var base_slot = 0;
switch(nodeData.name) {
case 'ApplyRegionalIPAdapters //Inspire':
input_name = "regional_ipadapter";
base_slot = 1;
break;
}
const onConnectionsChange = nodeType.prototype.onConnectionsChange;
nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
if(!link_info || type == 2)
return;
if(this.inputs[0].type == '*'){
const node = app.graph.getNodeById(link_info.origin_id);
let origin_type = node.outputs[link_info.origin_slot].type;
if(origin_type == '*') {
this.disconnectInput(link_info.target_slot);
return;
}
for(let i in this.inputs) {
let input_i = this.inputs[i];
if(input_i.name != 'select' && input_i.name != 'sel_mode')
input_i.type = origin_type;
}
}
if (!connected && (this.inputs.length > base_slot+1)) {
const stackTrace = new Error().stack;
if(
!stackTrace.includes('LGraphNode.prototype.connect') && // for touch device
!stackTrace.includes('LGraphNode.connect') && // for mouse device
!stackTrace.includes('loadGraphData')) {
this.removeInput(index);
}
}
let slot_i = 1;
for (let i = base_slot; i < this.inputs.length; i++) {
let input_i = this.inputs[i];
input_i.name = `${input_name}${slot_i}`
slot_i++;
}
let last_slot = this.inputs[this.inputs.length - 1];
if (last_slot.link != undefined) {
this.addInput(`${input_name}${slot_i}`, this.inputs[base_slot].type);
}
}
}
}});

28
custom_nodes/comfyui-inspire-pack/js/seed.js Normal file
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import { api } from "../../scripts/api.js";
function globalSeedHandler(event) {
let nodes = app.graph._nodes_by_id;
for(let i in nodes) {
let node = nodes[i];
if(node.type == 'GlobalSeed //Inspire') {
if(node.widgets) {
const w = node.widgets.find((w) => w.name == 'value');
const last_w = node.widgets.find((w) => w.name == 'last_seed');
last_w.value = w.value;
if(event.detail.value != null)
w.value = event.detail.value;
}
}
else
if(node.widgets) {
const w = node.widgets.find((w) => (w.name == 'seed' || w.name == 'noise_seed') && w.type == 'number');
if(w && event.detail.seed_map[node.id] != undefined) {
w.value = event.detail.seed_map[node.id];
}
}
}
}
api.addEventListener("inspire-global-seed", globalSeedHandler);

2
custom_nodes/comfyui-inspire-pack/prompts/example/prompt1.txt Normal file
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positive:beautiful scenery nature glass bottle landscape, , purple galaxy bottle,
negative:text, watermark

12
custom_nodes/comfyui-inspire-pack/prompts/example/prompt2.txt Normal file
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@@ -0,0 +1,12 @@
positive:1girl is walking through street,
raincoat, yellow umbrella
negative:text, watermark
-----------------
positive:museum, people are looking paintings, abstract
negative:text, watermark
----
positive:battle ground of space ships
negative:text, watermark

0
custom_nodes/comfyui-inspire-pack/prompts/put_prompts_dirs_here Normal file
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15
custom_nodes/comfyui-inspire-pack/pyproject.toml Normal file
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@@ -0,0 +1,15 @@
[project]
name = "comfyui-inspire-pack"
description = "This extension provides various nodes to support Lora Block Weight, Regional Nodes, Backend Cache, Prompt Utils, List Utils, Noise(Seed) Utils, ... and the Impact Pack."
version = "1.23"
license = { file = "LICENSE" }
dependencies = ["matplotlib", "cachetools"]
[project.urls]
Repository = "https://github.com/ltdrdata/ComfyUI-Inspire-Pack"
# Used by Comfy Registry https://comfyregistry.org
[tool.comfy]
PublisherId = "drltdata"
DisplayName = "ComfyUI Inspire Pack"
Icon = ""

5
custom_nodes/comfyui-inspire-pack/requirements.txt Normal file
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matplotlib
cachetools
numpy
webcolors
opencv-python

20
custom_nodes/comfyui-inspire-pack/resources/lbw-preset.custom.txt.example Normal file
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@@ -0,0 +1,20 @@
SD-BODY:1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1
SD-BODY0.5:1,1,1,1,1,1,0.2,1,0.2,0,0,0.8,1,1,1,1,1
SD-FACE:1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0
SD-FACE0.5:1,0,0,0,0,0,0,0,0.8,1,1,0.2,0,0,0,0,0
SD-FACE0.2:1,0,0,0,0,0,0,0,0.2,0.6,0.8,0.2,0,0,0,0,0
SD-HAND:1,0,1,1,0.2,0,0,0,0,0,0,0,0,0,0,0,0
SD-CLOTHING:1,1,1,1,1,0,0.2,0,0.8,1,1,0.2,0,0,0,0,0
SD-POSE:1,0,0,0,0,0,0.2,1,1,1,0,0,0,0,0,0,0
SD-PALETTE:1,0,0,0,0,0,0,0,0,0,0,0.8,1,1,1,1,1
SD-KEEPCHAR:1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,0,0
SD-KEEPBG:1,1,1,1,1,1,0.2,1,0.2,0,0,0.8,1,1,1,0,0
SD-REDUCEFIT:1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1
SD-LyCOBODY:1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1,1,1,1,1,1
SD-LyCOBODY0.5:1,1,1,1,1,1,1,1,1,0.2,0.2,0.5,1,1,0,0,0,0.2,0,0,0.8,1,1,1,1
SD-LyCOFACE:1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,0,0,0,0,0
SD-LyCOFACE0.5:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0.5,0.8,1,1,1,0.2,0,0,0,0,0
SD-LyCOCLOTH:1,1,1,1,1,1,1,1,0,0.2,0.2,0.2,0,0,0,0,0.5,0.8,1,1,0.2,0,0,0,0,0
SD-LyCOPOSE:1,0,0,0,0,0,0,0,0,0.2,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0
SD-LyCOKEEPBG:1,1,1,1,1,1,1,1,1,0.2,0.4,0.8,1,1,0.8,0.4,0.2,0.2,0,0,0.8,1,1,1,0,0
SD-LyCOPALETTE:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,1,1,1,1,1

122
custom_nodes/comfyui-inspire-pack/resources/lbw-preset.txt Normal file
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SD-NONE:0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
SD-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
SD-INS:1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0
SD-IND:1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0
SD-INALL:1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0
SD-MIDD:1,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0
SD-MIDD0.2:1,0,0,0,0,0,0.2,0.4,0.4,0.2,0,0,0,0,0,0,0
SD-MIDD0.8:1,0,0,0,0,0.5,0.8,0.8,0.4,0,0,0,0,0,0,0,0
SD-MOUT:1,0,0,0,0,0,1,1,1,1,1,1,1,1,0.5,0,0
SD-OUTD:1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0
SD-OUTS:1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1
SD-OUTALL:1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1
SD-ROUT:1,1,1,1,1,1,1,1,R,R,R,R,R,R,R,R,R
SD-AOUT:A,1,1,1,1,1,1,1,1,1,1,1,A,A,A,A,A
SD-AB:A,B,B,B,B,B,B,B,B,B,B,B,A,A,A,A,A
SD-ALL0.5:0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
SD-LyC-NONE:0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
SD-LyC-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
SD-LyC-INALL:1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
SD-LyC-MIDALL:1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0
SD-LyC-OUTALL:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1
SDXL-NONE:0,0,0,0,0,0,0,0,0,0,0,0
SDXL-ALL:1,1,1,1,1,1,1,1,1,1,1,1
SDXL-INALL:1,1,1,1,1,0,0,0,0,0,0,0
SDXL-MIDALL:1,0,0,0,0,1,0,0,0,0,0,0
SDXL-OUTALL:1,0,0,0,0,0,1,1,1,1,1,1
SDXL-LyC-NONE:0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
SDXL-LyC-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
SDXL-LyC-INALL:1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0
SDXL-LyC-MIDALL:1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0
SDXL-LyC-OUTALL:1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1
FLUX-DBL-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
FLUX-DBL-FRONT7:1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0
FLUX-DBL-MID6:1,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0
FLUX-DBL-TAIL6:1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1
FLUX-SINGLE-ALL:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
FLUX-SINGLE-1to10:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
FLUX-SINGLE-11to20:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
FLUX-SINGLE-21to30:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0
FLUX-SINGLE-31to37:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1
FLUX-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
@SD-FULL-TEST:17
@SD-BLOCK1-TEST:17,12,1
@SD-BLOCK2-TEST:17,12,2
@SD-BLOCK3-TEST:17,12,3
@SD-BLOCK4-TEST:17,12,4
@SD-BLOCK5-TEST:17,12,5
@SD-BLOCK6-TEST:17,12,6
@SD-BLOCK7-TEST:17,12,7
@SD-BLOCK8-TEST:17,12,8
@SD-BLOCK9-TEST:17,12,9
@SD-BLOCK10-TEST:17,12,10
@SD-BLOCK11-TEST:17,12,11
@SD-BLOCK12-TEST:17,12,12
@SD-BLOCK13-TEST:17,12,13
@SD-BLOCK14-TEST:17,12,14
@SD-BLOCK15-TEST:17,12,15
@SD-BLOCK16-TEST:17,12,16
@SD-BLOCK17-TEST:17,12,17
@SD-LyC-FULL-TEST:27
@SDXL-FULL-TEST:12
@SDXL-LyC-FULL-TEST:21
@FLUX-DBL-FULL:19
@FLUX-DBL-SGL-FULL:58
@FLUX-DBL0-TEST:19,14,2
@FLUX-DBL1-TEST:19,14,3
@FLUX-DBL2-TEST:19,14,4
@FLUX-DBL3-TEST:19,14,5
@FLUX-DBL4-TEST:19,14,6
@FLUX-DBL5-TEST:19,14,7
@FLUX-DBL6-TEST:19,14,8
@FLUX-DBL7-TEST:19,14,9
@FLUX-DBL8-TEST:19,14,10
@FLUX-DBL9-TEST:19,14,11
@FLUX-DBL10-TEST:19,14,12
@FLUX-DBL11-TEST:19,14,13
@FLUX-DBL12-TEST:19,14,14
@FLUX-DBL13-TEST:19,14,15
@FLUX-DBL14-TEST:19,14,16
@FLUX-DBL15-TEST:19,14,17
@FLUX-DBL16-TEST:19,14,18
@FLUX-DBL17-TEST:19,14,19
@FLUX-DBL18-TEST:19,14,20
@FLUX-SGL0-TEST:58,6,21
@FLUX-SGL1-TEST:58,6,22
@FLUX-SGL2-TEST:58,6,23
@FLUX-SGL3-TEST:58,6,24
@FLUX-SGL4-TEST:58,6,25
@FLUX-SGL5-TEST:58,6,26
@FLUX-SGL6-TEST:58,6,27
@FLUX-SGL7-TEST:58,6,28
@FLUX-SGL8-TEST:58,6,29
@FLUX-SGL9-TEST:58,6,30
@FLUX-SGL10-TEST:58,6,31
@FLUX-SGL11-TEST:58,6,32
@FLUX-SGL12-TEST:58,6,33
@FLUX-SGL13-TEST:58,6,34
@FLUX-SGL14-TEST:58,6,35
@FLUX-SGL15-TEST:58,6,36
@FLUX-SGL16-TEST:58,6,37
@FLUX-SGL17-TEST:58,6,38
@FLUX-SGL18-TEST:58,6,39
@FLUX-SGL19-TEST:58,6,40
@FLUX-SGL20-TEST:58,6,41
@FLUX-SGL21-TEST:58,6,42
@FLUX-SGL22-TEST:58,6,43
@FLUX-SGL23-TEST:58,6,44
@FLUX-SGL24-TEST:58,6,45
@FLUX-SGL25-TEST:58,6,46
@FLUX-SGL26-TEST:58,6,47
@FLUX-SGL27-TEST:58,6,48
@FLUX-SGL28-TEST:58,6,49
@FLUX-SGL29-TEST:58,6,50
@FLUX-SGL30-TEST:58,6,51
@FLUX-SGL31-TEST:58,6,52
@FLUX-SGL32-TEST:58,6,53
@FLUX-SGL33-TEST:58,6,54
@FLUX-SGL34-TEST:58,6,55
@FLUX-SGL35-TEST:58,6,56
@FLUX-SGL36-TEST:58,6,57
@FLUX-SGL37-TEST:58,6,58
@FLUX-SGL38-TEST:58,6,59

1048
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