[ { "path": ".github/workflows/publish_action.yml", "content": "name: Publish to Comfy registry\non:\n workflow_dispatch:\n push:\n branches:\n - main\n paths:\n - \"pyproject.toml\"\n\njobs:\n publish-node:\n name: Publish Custom Node to registry\n runs-on: ubuntu-latest\n steps:\n - name: Check out code\n uses: actions/checkout@v4\n - name: Publish Custom Node\n uses: Comfy-Org/publish-node-action@main\n with:\n personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}" }, { "path": ".gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. 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\n**Works ONLY with SDXL checkpoints**\n
\n
\n![image](https://github.com/user-attachments/assets/0c97dccf-ac8a-43f7-b50b-8bbf7ed81049)\n\n![image](https://github.com/user-attachments/assets/bbc88aaf-fba4-43f1-80ea-fece379308db)\n\n\n\n## Installation guide\n[About](#comfyui-instantid-faceswap-v011) | [Installation guide](#installation-guide) | [Custom nodes](#custom-nodes) | [Workflows](#workflows) | [Tips](#tips) | [Changelog](#changelog)\n\n1. Clone or download this repository and put it into **ComfyUI/custom_nodes**\n2. Open commandline in the **ComfyUI/custom_nodes/comfyui-instantId-faceswap/** folder and type `pip install -r requirements.txt` to install dependencies\n3. Manually download required files and create required folders:\n - [antelopev2 models](https://huggingface.co/DIAMONIK7777/antelopev2/tree/main)\n and put them into **ComfyUI/models/insightface/models/antelopev2** folder\n - 1k3d68.onnx\n - 2d106det.onnx\n - genderage.onnx\n - glintr100.onnx\n - scrfd_10g_bnkps.onnx\n\n - [IpAdapter and ControlNet](https://huggingface.co/InstantX/InstantID/tree/main)\n - ip-adapter.bin - put it into **ComfyUI/models/ipadapter**\n - ControlNetModel/diffusion_pytorch_model.safetensors and ControlNetModel/config.json - put those files in new folder in **ComfyUI/models/controlnet**\n\nNewly added files hierarchy should look like this:\n```\nComfyUI\n\\---models\n \\---ipadapter\n ipadapter.bin\n \\---controlnet\n \\--- FOLDER_YOU_CREATED\n config.json\n diffusion_pytorch_model.safetensors\n \\---insightface\n \\---models\n \\antelopev2\n 1k3d68.onnx\n 2d106det.onnx\n genderage.onnx\n glintr100.onnx\n scrfd_10g_bnkps.onnx\n```\n\n*Note You don't need to add the 'ipadapter', and 'controlnet' folders to this specific location if you already have them somewhere else (also you can rename ipadapter.bin and ControlNetModel to something of your liking).\nInstead, You can edit `ComfyUI/extra_model_paths.yaml` and add folders containing those files to the config.\n\n## Custom nodes\n[About](#comfyui-instantid-faceswap-v011) | [Installation guide](#installation-guide) | [Custom nodes](#custom-nodes) | [Workflows](#workflows) | [Tips](#tips) | [Changelog](#changelog)\n\n- ### Load Insightface:\n Loads Insightface. Models need to be in a specific location. Check the [Installation guide](#installation-guide) for details.\n\n- ### Load instantId adapter:\n Loads the InstantId adapter and resampler. The model needs to be in a specific location. Check the [Installation guide](#installation-guide) for details. The resampler is used to prepare face embeds for ControlNet and the adapter.\n\n- ### Apply instantId adapter:\n Applies the InstantId adapter to the model. This is optional—you can achieve good results without using this node.\n\n **Params:**\n - **checkpoint** - SDXL checkpoint\n - **instantId_adapter** - intantId adapter\n - **face_conditioning** - face conditioning prepared by the resampler\n - **strength** - strength of the instantId adapter\n\n- ### Apply instantId ControlNet:\n Applies InstantId ControlNet.\n\n **Params:**\n - **positive** - positive prompts\n - **negative** - negative prompts\n - **face_conditioning** - face conditioning prepared by the resampler\n - **control_net** - instantId Controlnet\n - **strength** - strength of instantId ControlNet\n\n- ### Apply instantId and ControlNet:\n A subgraph node that bundles several operations into a single node for convenience. It includes the following nodes: LoadInstantIdAdapter, FaceEmbedCombine, ControlNetLoader, InstantIdAdapterApply, and ControlNetInstantIdApply.\n\n This node streamlines the process by loading the InstantId adapter, combining face embeddings, loading the ControlNet, and applying both the InstantId adapter and ControlNet in one step.\n\n- ### FaceEmbed for instantId\n Prepares face embeds for generation. You can chain multiple face embeds.\n\n **Params:**\n - **insightface** - insightface\n - **face_image** - input image from which to extract embed data\n - **face_embeds** (*optional*) - additional face embed(s)\n\n- ### FaceEmbed Combine\n Prepares face embeds for ControlNet and the adapter.\n\n **Params:**\n - **resampler** - resampler\n - **face_embeds** - face_embeds\n\n- ### Get Angle from face\n Returns the angle (in degrees) by which the image must be rotated counterclockwise to align the face. Since there can be more than one face in the image, face search is performed only in the area of the drawn mask, enlarged by the pad parameter.\n\n **Note:** If the face is rotated by an extreme angle, insightface won't be able to find the correct position of face keypoints, so the rotation angle might not always be accurate. In these cases, manually draw your own KPS.\n\n\n **Params:**\n - **insightface** - insightface\n - **image** - image with the face to rotate\n - **mask** - mask\n - **rotate_mode** - available options:\n - *none* - returns 0\n - *loseless* - returns the closest angle to 90, 180, 270 degrees\n - *any* - returns a specific angle by which the image should be rotated\n - **pad_top** - how many pixels to enlarge the mask upwards\n - **pad_right** - how many pixels to enlarge the mask to the right\n - **pad_bottom** - how many pixels to enlarge the mask downwards\n - **pad_left** - how many pixels to enlarge the mask to the left\n\n\n- ### Get Angle from KPS data\n Returns the angle (in degrees) by which the image must be rotated counterclockwise to align the face.\n\n **Params:**\n - **rotate_mode** - available options:\n - *none* - returns 0\n - *loseless* - returns the closest angle to 90, 180, 270 degrees\n - *any* - returns a specific angle by which the image should be rotated\n\n\n- ### Rotate Image\n Rotates the image by the given angle and expands it.\n\n **Params:**\n - **image** - image\n - **angle** - angle\n - **counter_clockwise** - direction\n\n- ### Remove rotation padding\n Removes the expanded region added by two rotations (first to align the face, and second to return to the original position).\n\n **Params:**\n - **original_image** - image before rotation\n - **rotated_image** - rotated image\n\n- ### Draw KPS\n Allows you to draw your own keypoints (KPS), useful when you get the error `\"No face detected in pose image\"` or when using InstantId to generate images from prompts only. Click and drag the KPS to move them around.\n\n When you place your KPS in the desired position, this node will show the angle by which the image should be rotated to align the face.\n\n You can adjust the opacity of each keypoint to sacrifice likeness, for example, when adding \"glasses\".\n\n **Shortcuts:**\\\n **CTRL + DRAG** - move around\\\n **CTRL + WHEEL** - zoom in / out\\\n **ALT + WHEEL** - decrease / increase distance of other points from blue point (nose kps)\n\n **Params:**\n - **image_reference** (optional) - an image that serves as a background to more accurately match the appropriate points. If provided, the resulting image will have the width and height of this image.\n - **width** - width of the image (disabled if `image_reference` is provided)\n - **height** - height of the image (disabled if `image_reference` is provided)\n\n- ### 3d KPS from image\n Allows you to extract 3D keypoints (KPS) of a face from an image. This is useful when generating content from text prompts and wanting to rotate the face while preserving the distance between the eyes, nose, and mouth.\n\n To use it, connect the `image` node, then click the **\"Get KPS From Image\"** button. Afterward, you can adjust the scale, position, and rotation of the face by clicking the **\"Change KPS\"** button.\n\n **IMPORTANT:** \n - Clicking Get KPS From Image will run InsightFace to extract KPS data, so it’s best not to use this in the middle of the generation process (this depends on your system's performance).\n - You cannot manually change the distance between KPS in this node.\n\n Once your KPS are placed in the desired position, the node will show the angle by which the image should be rotated to align the face.\n\n You can adjust the opacity of each keypoint to sacrifice likeness, for example, when adding \"glasses\".\n\n **Shortcuts:**\\\n **CTRL + DRAG** - move around\\\n **CTRL + WHEEL** - zoom in / out\\\n **ALT + WHEEL** - scale KPS\n\n **Params:**\n - **image** - an image of face from which KPS will be calculated\n - **width** - width of the image\n - **height** - height of the image\n\n\n- ### Preprocess image for instantId:\n Cuts out the mask area wrapped in a square, enlarges it in each direction by the `pad` parameter, and resizes it (to dimensions rounded down to multiples of 8). It also creates a control image for InstantId ControlNet.\n\n **Note:** If the face is rotated by an extreme angle, the prepared `control_image` may be drawn incorrectly.\n\n If the `insightface` param is not provided, it will not create a control image, and you can use this node as a regular node for inpainting (to cut the masked region with padding and later compose it).\n\n **Params:**\n - **image** - your pose image (the image in which the face will be swapped)\n - **mask** - drawn mask (the area to be changed must contain the face; you can also mask other features like hair or hats and change them later with prompts)\n - **insightface** (optional) - loaded insightface\n - **width** - width of the image in pixels (check `resize_mode`)\n - **height** - height of the image in pixels, check `resize_mode`\n - **resize_mode** - available options:\n - *auto* - automatically calculates the image size so that the area is `width` x ` height`.\n For SDXL, you probably want to use this option with:\n **width: 1024, height: 1024**\n - *scale by width* - ignores provided `height` and calculates it based on the aspect ratio\n - *scale by height* - ignores provided `width` and calculates it based on the aspect ratio\n - *free* - uses the provided `width` and `height`\n - **pad** - how many pixels to enlarge the mask in each direction\n\n- ### Preprocess image for instantId (Advanced):\n Same as **Preprocess Image for InstantId** with five additional parameters.\n\n **Params:**\n - **upscale_method** - *nearest-exact*, *bilinear*, *area*, *bicubic*, *lanczos*\n - **pad_top** - how many pixels to enlarge the mask upwards\n - **pad_right** - how many pixels to enlarge the mask to the right\n - **pad_bottom** - how many pixels to enlarge the mask downwards\n - **pad_left** - how many pixels to enlarge the mask to the left\n\n\n- ### Randomize 2d KPS\n Randomizes the position, angle, and rotation of the KPS based on the provided parameters.\n\n **Params:**\n - **angle_min** - minimum rotation angle. The rotation point is the center of the KPS,\n - **angle_max** - maximum rotation angle, the rotation point is the center of the KPS,\n - **scale_min** - minimum scaling value relative to the KPS center (1 means no scaling),\n - **scale_max** - maximum scaling value relative to the KPS center (1 means no scaling),\n - **translate_x** - value by which to shift the KPS along the X axis. For example, setting it to 200 will randomly select a shift value between -200 and 200,\n - **translate_y** - value by which to shift the KPS along the Y axis. For example, setting it to 200 will randomly select a shift value between -200 and 200,\n - **border** - edge threshold; if the KPS gets too close to the image border during translation, rotation, or scaling, it will be repositioned to this value\n\n\n- ### Randomize 3d KPS\n Randomizes the rotation of the KPS around three axes. Setting any of the parameters will randomly select a rotation angle around the corresponding axis. The rotation point is the center of the KPS.\n Example: Setting rotate_x to 20 will rotate the KPS by a random angle between -20 and 20 degrees.\n\n **Params:**\n - **rotate_x** - rotation angle around the X axis,\n - **rotaet_y** - rotation angle around the Y axis,\n - **rotate_z** - rotation angle around the Z axis,\n\n\n- ### Scale 2d KPS by\n Scales the KPS data by a given factor.\n\n **Params:**\n - **scale**: scaling factor\n\n\n- ### Scale 2d KPS\n Scales the KPS data to the specified width and height.\n\n **Params:**\n - **width**: desired width,\n - **height**: desired height\n\n\n- ### Rotate 2d KPS\n Rotates the KPS by the given angle and expands it.\n\n **Params:**\n - **angle**: rotation angle\n\n\n - ### Crop 2d KPS\n Crops the KPS.\n\n **Params:**\n - **x**: X coordinate of the top-left corner,\n - **y**: Y coordinate of the top-left corner,\n - **width**: width of the cropped area,\n - **height**: height of the cropped area,\n\n\n - ### Create KPS Image\n Creates a control_image from `kps_data`\n\n\n - ### Create mask from Kps\n Creates a mask based on the KPS position.\n\n **Params:**\n - **grow_by** - expands the mask by adding extra space on all sides. The additional margin on each side is equal to the mask’s dimension divided by this value. For example, if the KPS width is 20 pixels and grow_by is set to 10, an extra 20/10 (i.e., 2 pixels) will be added to the left and right sides — resulting in a new width of 20 + 2 + 2 = 24 pixels. The same applies to the height.\n\n## Workflows\n[About](#comfyui-instantid-faceswap-v011) | [Installation guide](#installation-guide) | [Custom nodes](#custom-nodes) | [Workflows](#workflows) | [Tips](#tips) | [Changelog](#changelog)\n\n\nYou can find example workflows in the `/workflows` folder.\nNodes colors legend: \\\n**yellow** - node from this extension,\\\n**blue** - inputs, load your controlnets, models, images ...\\\n**purple** - you might want to configure those\\\n**cyan** - output images\\\n**green** - positive prompts\\\n**red** - negative prompts\n\nIf you set the mask blur options remember that it will shrink the area you masked\n\n### simple.json\nFace swap: Set your pose image, draw a mask, set your face reference (the face that will replace the masked area in the pose image), and that's it.\n\n### simple_with_adapter.json\nSame as `simple.json` with an additional node `Apply instantId adapter`\n\n### simple_two_embeds.json\nSame as `simple.json`, but allows you to provide two face references. You can use this to merge two different faces or just provide a second reference for the first face.\n\n### draw_kps.json\nFace swap: Set your pose image, draw a mask, set your face reference (the face that will replace the masked area in the pose image), and then click the \"draw KPS\" button on the `Draw KPS` node to set your KPS.\n\n
\n View Example Image\n\n### draw_kps_rotate.json\nSame as `draw_kps.json`, but it will also rotate the pose image. After setting your KPS, you should set the angle by which you want to rotate the image to align the face properly.\n\n
\n View Example Image\n\n### auto_rotate.json\nSame as `simple_with_adapter.json`, but it will automatically detect the angle of rotation based on the mask and padding set in the `Get Angle from Face` node.\n\n### promp2image.json\nGenerates an image based only on the face reference and prompts. Set your face reference, draw the KPS where the face should be drawn, and add prompts like \"man sitting in the park.\"\n\n### promp2image_detail_pass.json\nSame as `prompt2image.json`, but this one expects the KPS you draw to be small, so the face is not detailed (or may even be deformed). Second pass should fix the face.\n\n\n### prompts2img_2faces_enhancement.json\nA workflow that generates two faces in one image and enhances them one by one.\nSet your face references and KPS for one image, then set a second KPS in another region of the picture. Good results depend on your prompts.\n\n
\n View Example Image\n \n ![Two KPS one flow](https://github.com/user-attachments/assets/fbaa38df-3400-401d-b644-087723e6488c)\n
\n\n### inpaint.json\nSince you can use the `Preprocess Image for InstantId` and `Preprocess Image for InstantId (Advanced)` nodes to resize your images with a mask, this workflow is useful for inpainting in general. This workflow shows you how to do it.\n\n
\n View Example Image\n \n ![basic inpaint](https://github.com/user-attachments/assets/bda258b1-a988-47f5-beb6-105289c990ac)\n\n
\n\n### prop2image_latent_upscale.json\nSimilar to `promp2image_detail_pass.json`, this workflow allows you to draw your KPS. The workflow will run a first pass at 50%, upscale the latent by `1.4`, and finish with a detail pass on the face area.\n\n### prop2image_latent_upscale_with_2d_randomizer.json\nSame as `prop2image_latent_upscale.json`, but it will randomize the position of the face within the image.\n\n### prop2image_latent_upscale_with_3d_and_2d_randomizer.json\nSimilar to `prop2image_latent_upscale_with_2d_randomizer.json`, but instead of drawing your KPS, you retrieve them from a face image (in 3d).\nClick the **\"Get KPS from image\"** button on the 3D KPS from Image node, then use the **\"Change KPS\"** button to adjust the position of your KPS. You can also randomize the 3D rotation in this workflow.\n\n### prop2image_latent_upscale_with_3d_and_2d_randomizer_with_rotation.json\nExactly the same as `prop2image_latent_upscale_with_3d_and_2d_randomizer.json`, with one small addition: you can set the face rotation method during the last pass. The workflow will rotate the face to a \"straight\" position, process the image, and then composite it into the final result.\n\nThe default rotation is set to \"any,\" so you might encounter some artifacts from the rotation. You can adjust this setting as needed.\n\n## Tips\n[About](#comfyui-instantid-faceswap-v011) | [Installation guide](#installation-guide) | [Custom nodes](#custom-nodes) | [Workflows](#workflows) | [Tips](#tips) | [Changelog](#changelog)\n\n- Most workflows require you to draw a mask on the pose image.\n- If you encounter the error `No face detected in pose image`, try drawing a larger mask or increasing the `pad` parameter or draw KPS yourself.\n- You can adjust the opacity of each keypoint to preserve original features or rely more on your prompts without sacrificing the overall likeness of the face.\n- You can modify more than just the face — add accessories like a hat, change hair, or even alter expressions.\n- If you're changing a lot of elements unrelated to the face, it's a good idea to add a second pass focused primarily on the face area to enhance detail.\n- To improve results, you can integrate other extensions such as ControlNet for inpainting, Fooocus inpaint, FaceShaper, Expression Lora, and many more.\n- To understand the relationship between ControlNet and the adapter, check the official paper linked in the instantId repository: https://github.com/instantX-research/InstantID?tab=readme-ov-file\n\n## Changelog\n[About](#comfyui-instantid-faceswap-v011) | [Installation guide](#installation-guide) | [Custom nodes](#custom-nodes) | [Workflows](#workflows) | [Tips](#tips) | [Changelog](#changelog)\n\n- ### 0.1.1 (03.03.2025)\n - Introduced new nodes to ensure proper KPS size.\n - Removed the ability to draw masks on the KPS node (a separate node is available for this).\n - Added the ability to add transparency to individual KPS, providing similar functionality as `ControlNet Scale` but for specific parts of the face (e.g., adding glasses).\n - The generation of KPS control images has been entirely moved to the backend (Python). You can still draw KPS manually, but this change reduces the creation of temporary files.\n - Drawn KPS positions are now saved into the workflow.\n - Added the ability to import 3D KPS positions from the face, allowing rotation, scaling, and movement of those points while preserving the distance between the eyes, nose, and mouth.\n - Added options to randomize position, rotation, and KPS scaling to diversify final images.\n - Added the ability to randomize 3D face rotation to further diversify results.\n - To diversify results even further, you can use tools like [comfyui-text-randomizer](https://github.com/nosiu/comfyui-text-randomizer), which was created as a side project during the development of this repository.\n\n **Note:** Some old workflows will not be compatible with this version.\n\n\n- ### 0.1.0 (20.10.2024)\n - The code was rewritten from scratch and now uses the ComfyUI backend. This allows you to chain LORAs or ControlNets as needed, providing greater control over the entire process.\nFor example, you can now draw your own KPS, enabling both text-to-image and image-to-image generation.\n - Removed most dependencies (including Diffusers).\n - Removed all old nodes and introduced new ones.\n - The script that automatically generated workflows based on all faces in a specific catalog has been removed.\n\n **Note:** Old workflows will not work with this version.\n\n\n- ### 0.0.5 (25.02.2024)\n - The `mask_strength` parameter has been fixed; it now functions correctly. Previously, it was stuck at *0.9999* regardless of the chosen value.\n - The `ip_adapter_scale` parameter has been fixed. If you were using the xformers, this parameter could be stuck at *50*.\n - Changed the method of processing face_embed(s).\n - Added the `rotate_face` parameter. It will attempt to rotate the image to keep the face straight before processing and rotate it back to the original position afterward.\n\n- ### 0.0.4 (14.02.2024)\n - To save memory, you can run Comfy with the `--fp16-vae` argument to disable the default VAE upcasting to float32.\n - Merged the old `resize` and `resize_to` options into just `resize` for the Faceswap generate node. To emulate the old behavior where resize was unchecked, select `don't`.\n - Added a manual offload mechanism to save GPU memory.\n - Changed the minimum and maximum values for `mask_strength` to range from 0.00 to 1.00.\n- ### 0.0.3 (07.02.2024)\n - Fixed an error that caused new face_embeds to be added when editing previous ones\n- ### 0.0.2 (05.02.2024)\n - Introducing workflow generator script - [more information here](#workflow-script-beta)\n - Updating the dependency diffusers to version 0.26.x. Run either:\n ```bash\n pip install -r requirements.txt\n ```\n or\n ```bash\n pip install -U diffusers~=0.26.0\n ```\n\n- ### 0.0.1 (01.02.2024)\n - Progress bar and latent preview added for generation node\n\n" }, { "path": "__init__.py", "content": "from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS\nWEB_DIRECTORY = './ui/'\n\n__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY']" }, { "path": "ip_adapter/instantId.py", "content": "import torch\nfrom comfy.ldm.modules.attention import optimized_attention\n\nclass InstantId(torch.nn.Module):\n def __init__(self, ip_adapter):\n super().__init__()\n\n self.to_kvs = torch.nn.ModuleDict()\n\n for key, value in ip_adapter.items():\n k = key.replace(\".weight\", \"\").replace(\".\", \"_\")\n self.to_kvs[k] = torch.nn.Linear(value.shape[1], value.shape[0], bias=False)\n self.to_kvs[k].weight.data = value\n\n\n# based on https://github.com/laksjdjf/IPAdapter-ComfyUI/blob/main/ip_adapter.py#L256\nclass CrossAttentionPatch:\n def __init__(self, scale, instantId, cond, number):\n self.scales = [scale]\n self.instantIds = [instantId]\n self.conds = [cond]\n self.number = number\n\n def set_new_condition(self, scale, instantId, cond, number):\n self.scales.append(scale)\n self.instantIds.append(instantId)\n self.conds.append(cond)\n self.number = number\n\n def __call__(self, q, k, v, extra_options):\n dtype = torch.float16\n hidden_states = optimized_attention(q, k, v, extra_options[\"n_heads\"])\n for scale, cond, instantId in zip(self.scales, self.conds, self.instantIds):\n k_cond = instantId.to_kvs[str(self.number*2+1) + \"_to_k_ip\"](cond).to(dtype=dtype)\n v_cond = instantId.to_kvs[str(self.number*2+1) + \"_to_v_ip\"](cond).to(dtype=dtype)\n ip_hidden_states = optimized_attention(q, k_cond, v_cond, extra_options[\"n_heads\"])\n hidden_states = hidden_states + ip_hidden_states * scale\n return hidden_states.to(dtype=dtype)" }, { "path": "ip_adapter/resampler.py", "content": "# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py\nimport math\n\nimport torch\nimport torch.nn as nn\n\n# FFN\ndef FeedForward(dim, mult=4):\n inner_dim = int(dim * mult)\n return nn.Sequential(\n nn.LayerNorm(dim),\n nn.Linear(dim, inner_dim, bias=False),\n nn.GELU(),\n nn.Linear(inner_dim, dim, bias=False),\n )\n\ndef reshape_tensor(x, heads):\n bs, length, _ = x.shape\n #(bs, length, width) --> (bs, length, n_heads, dim_per_head)\n x = x.view(bs, length, heads, -1)\n # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)\n x = x.transpose(1, 2)\n # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)\n x = x.reshape(bs, heads, length, -1)\n return x\n\n\nclass PerceiverAttention(nn.Module):\n def __init__(self, *, dim, dim_head=64, heads=8):\n super().__init__()\n self.scale = dim_head**-0.5\n self.dim_head = dim_head\n self.heads = heads\n inner_dim = dim_head * heads\n\n self.norm1 = nn.LayerNorm(dim)\n self.norm2 = nn.LayerNorm(dim)\n\n self.to_q = nn.Linear(dim, inner_dim, bias=False)\n self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)\n self.to_out = nn.Linear(inner_dim, dim, bias=False)\n\n def forward(self, x, latents):\n \"\"\"\n Args:\n x (torch.Tensor): image features\n shape (b, n1, D)\n latent (torch.Tensor): latent features\n shape (b, n2, D)\n \"\"\"\n x = self.norm1(x)\n latents = self.norm2(latents)\n\n b, l, _ = latents.shape\n\n q = self.to_q(latents)\n kv_input = torch.cat((x, latents), dim=-2)\n k, v = self.to_kv(kv_input).chunk(2, dim=-1)\n\n q = reshape_tensor(q, self.heads)\n k = reshape_tensor(k, self.heads)\n v = reshape_tensor(v, self.heads)\n\n # attention\n scale = 1 / math.sqrt(math.sqrt(self.dim_head))\n weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards\n weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)\n out = weight @ v\n\n out = out.permute(0, 2, 1, 3).reshape(b, l, -1)\n\n return self.to_out(out)\n\n\nclass Resampler(nn.Module):\n def __init__(\n self,\n dim=1024,\n depth=8,\n dim_head=64,\n heads=16,\n num_queries=8,\n embedding_dim=768,\n output_dim=1024,\n ff_mult=4,\n ):\n super().__init__()\n\n self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)\n\n self.proj_in = nn.Linear(embedding_dim, dim)\n\n self.proj_out = nn.Linear(dim, output_dim)\n self.norm_out = nn.LayerNorm(output_dim)\n\n self.layers = nn.ModuleList([])\n for _ in range(depth):\n self.layers.append(\n nn.ModuleList(\n [\n PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),\n FeedForward(dim=dim, mult=ff_mult),\n ]\n )\n )\n\n def forward(self, x):\n\n latents = self.latents.repeat(x.size(0), 1, 1)\n\n x = self.proj_in(x)\n\n for attn, ff in self.layers:\n latents = attn(x, latents) + latents\n latents = ff(latents) + latents\n\n latents = self.proj_out(latents)\n return self.norm_out(latents)" }, { "path": "nodes.py", "content": "import os\nimport cv2\nimport torch\nimport numpy as np\nimport json\nimport comfy.utils\nimport folder_paths\nfrom urllib.parse import urlparse, parse_qs\nfrom server import PromptServer\nfrom aiohttp import web\nfrom comfy_execution.graph_utils import GraphBuilder\nfrom .ip_adapter.resampler import Resampler\nfrom .ip_adapter.instantId import InstantId\nfrom insightface.app import FaceAnalysis\nfrom .utils import draw_kps, set_model_patch_replace, resize_to_fit_area, \\\n kps_rotate_2d, kps_rotate_3d, kps3d_to_kps2d, calculate_size_after_rotation, \\\n get_mask_bbox_with_padding,get_kps_from_image,get_angle, image_rotate_with_pad, \\\n get_bbox_from_kps\n\n\nfolder_paths.folder_names_and_paths[\"ipadapter\"] = ([os.path.join(folder_paths.models_dir, \"ipadapter\")], folder_paths.supported_pt_extensions)\nINSIGHTFACE_PATH = os.path.join(folder_paths.models_dir, \"insightface\")\nCATEGORY_NAME = \"InstantId Faceswap\"\nMAX_RESOLUTION = 16384\n\n\n#==============================================================================\n# get key points and landmarks position as 3d points and send it back to frontend when requested\nroutes = PromptServer.instance.routes\n@routes.post('/get_keypoints_for_instantId')\nasync def proxy_handle(request):\n post = await request.json()\n\n try:\n app = FaceAnalysis(\n name=\"antelopev2\",\n root=INSIGHTFACE_PATH,\n providers=[\"CPUExecutionProvider\", \"CUDAExecutionProvider\"]\n )\n\n app.prepare(ctx_id=0, det_size=(640, 640))\n parsed_url = urlparse(post['image'])\n queries = parse_qs(parsed_url.query)\n path = os.path.join(folder_paths.get_directory_by_type(queries['type'][0]), queries['filename'][0])\n image = cv2.imread(path)\n faces = app.get(cv2.cvtColor(image, cv2.COLOR_RGB2BGR))\n if len(faces) == 0:\n raise Exception(\"No face detected\")\n\n face = sorted(faces, key=lambda x: (x[\"bbox\"][2] - x[\"bbox\"][0]) * (x[\"bbox\"][3] - x[\"bbox\"][1]))[-1] # only use the maximum face\n\n landmarks_3d = face.landmark_3d_68\n # KPS\n left_eye = np.mean(landmarks_3d[36:42], axis=0)\n right_eye = np.mean(landmarks_3d[42:48], axis=0)\n nose_tip = np.array([landmarks_3d[33][0], landmarks_3d[33][1], landmarks_3d[30][2]])\n left_mouth = landmarks_3d[48]\n right_mouth = landmarks_3d[54]\n \n return web.json_response({\n \"status\": \"ok\",\n \"data\": {\n \"kps\": [ left_eye.tolist(), right_eye.tolist(), nose_tip.tolist(), left_mouth.tolist(), right_mouth.tolist() ],\n \"jawline\": landmarks_3d[0:17].tolist(), \n \"eyebrow_left\": landmarks_3d[17:22].tolist(),\n \"eyebrow_right\": landmarks_3d[22:27].tolist(),\n \"nose_bridge\": landmarks_3d[27:31].tolist(), \n \"nose_lower\": landmarks_3d[31:36].tolist(), \n \"eye_left\": landmarks_3d[36:42].tolist(),\n \"eye_right\": landmarks_3d[42:48].tolist(),\n \"mouth_outer\": landmarks_3d[48:60].tolist(),\n \"mouth_inner\": landmarks_3d[60:68].tolist(),\n }\n })\n except Exception as error:\n if str(error) == \"No face detected\":\n return web.json_response({\n \"error\": \"No face detected\"\n })\n else:\n raise error\n\n\n#==============================================================================\nclass FaceEmbed:\n def __init__(self):\n pass\n\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"insightface\": (\"INSIGHTFACE_APP\",),\n \"face_image\": (\"IMAGE\",)\n },\n \"optional\": {\n \"face_embeds\": (\"FACE_EMBED\",)\n }\n }\n\n RETURN_TYPES = (\"FACE_EMBED\",)\n RETURN_NAMES = (\"face embeds\",)\n FUNCTION = \"make_face_embed\"\n CATEGORY = CATEGORY_NAME\n\n def make_face_embed(self, insightface, face_image, face_embeds = None):\n face_image = (255.0 * face_image.cpu().numpy().squeeze()).clip(0, 255).astype(np.uint8)\n face_info = insightface.get(cv2.cvtColor(face_image, cv2.COLOR_RGB2BGR))\n\n assert len(face_info) > 0, \"No face detected for face embed\"\n\n face_info = sorted(face_info, key=lambda x: (x[\"bbox\"][2] - x[\"bbox\"][0]) * (x[\"bbox\"][3] - x[\"bbox\"][1]))[-1] # only use the maximum face\n face_emb = torch.tensor(face_info[\"embedding\"], dtype=torch.float32).unsqueeze(0)\n\n if face_embeds is None:\n return (face_emb,)\n\n face_embeds = torch.cat((face_embeds, face_emb), dim=-2)\n return (face_embeds,)\n\n\n#==============================================================================\nclass FaceEmbedCombine:\n def __init__(self):\n pass\n\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"resampler\": (\"RESAMPLER\",),\n \"face_embeds\": (\"FACE_EMBED\",)\n },\n }\n\n RETURN_TYPES = (\"FACE_CONDITIONING\",)\n RETURN_NAMES = (\"face conditioning\",)\n FUNCTION = \"combine_face_embed\"\n CATEGORY = CATEGORY_NAME\n\n def combine_face_embed(self, resampler, face_embeds):\n embeds = torch.mean(face_embeds, dim=0, dtype=torch.float32).unsqueeze(0)\n embeds = embeds.reshape([1, -1, 512])\n conditionings = resampler(embeds).to(comfy.model_management.get_torch_device())\n return (conditionings,)\n\n\n#==============================================================================\nclass AngleFromFace:\n rotate_modes = [\"none\", \"loseless\", \"any\"]\n def __init__(self):\n pass\n\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"insightface\": (\"INSIGHTFACE_APP\",),\n \"image\": (\"IMAGE\", { \"tooltip\": \"Pose image.\" }),\n \"mask\": (\"MASK\",),\n \"rotate_mode\": (self.rotate_modes,),\n \"pad_top\": (\"INT\", {\"default\": 100, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"pad_right\": (\"INT\", {\"default\": 100, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"pad_bottom\": (\"INT\", {\"default\": 100, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"pad_left\": (\"INT\", {\"default\": 100, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n },\n }\n\n RETURN_TYPES = (\"FLOAT\",)\n RETURN_NAMES = (\"angle\",)\n FUNCTION = \"get_angle\"\n CATEGORY = CATEGORY_NAME\n\n def get_angle(\n self, insightface, image, mask, rotate_mode,\n pad_top, pad_right, pad_bottom, pad_left\n ):\n\n p_x1, p_y1, p_x2, p_y2 = get_mask_bbox_with_padding(mask.squeeze(0), pad_top, pad_right, pad_bottom, pad_left)\n image = image[:, p_y1:p_y2, p_x1:p_x2]\n kps = get_kps_from_image(image, insightface)\n\n angle = 0.\n if rotate_mode != \"none\" :\n angle = get_angle(\n kps[0], kps[1],\n round_angle = True if rotate_mode == \"loseless\" else False\n )\n return (angle,)\n\n\n#==============================================================================\nclass AngleFromKps:\n rotate_modes = [\"none\", \"loseless\", \"any\"]\n def __init__(self):\n pass\n\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"kps_data\": (\"KPS_DATA\",),\n \"rotate_mode\": (self.rotate_modes,)\n },\n }\n\n RETURN_TYPES = (\"FLOAT\",)\n RETURN_NAMES = (\"angle\",)\n FUNCTION = \"get_angle\"\n CATEGORY = CATEGORY_NAME\n\n def get_angle(self, kps_data, rotate_mode):\n kps_data = json.loads(kps_data)\n angle = 0.\n if rotate_mode != \"none\" :\n angle = get_angle(\n kps_data['array'][0], kps_data['array'][1],\n round_angle = True if rotate_mode == \"loseless\" else False\n )\n return (angle,)\n\n\n#==============================================================================\nclass ComposeRotated:\n def __init__(self):\n pass\n\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"original_image\": (\"IMAGE\",),\n \"rotated_image\": (\"IMAGE\",),\n }\n }\n\n RETURN_TYPES = (\"IMAGE\",)\n RETURN_NAMES = (\"image\",)\n FUNCTION = \"compose_rotate\"\n CATEGORY = CATEGORY_NAME\n\n def compose_rotate(self, original_image, rotated_image):\n original_width, original_height = original_image.shape[2], original_image.shape[1]\n rotated_width, rotated_height = rotated_image.shape[2], rotated_image.shape[1]\n\n if rotated_width != original_width:\n pad_x1 = (rotated_width - original_width) // 2\n pad_x2 = pad_x1 * -1\n else:\n pad_x1 = 0\n pad_x2 = original_width\n\n if rotated_height != original_height:\n pad_y1 = (rotated_height - original_height) // 2\n pad_y2 = pad_y1 * -1\n else:\n pad_y1 = 0\n pad_y2 = original_height\n\n image = rotated_image[:, pad_y1:pad_y2, pad_x1:pad_x2, :]\n return (image,)\n\n\n#==============================================================================\nclass RotateImage:\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"image\": (\"IMAGE\",),\n \"angle\": (\"FLOAT\", {\"default\": 0.0, \"min\": -360.0, \"step\": 0.1, \"max\": 360.0},),\n \"counter_clockwise\": (\"BOOLEAN\", {\"default\": True},),\n }\n }\n\n RETURN_TYPES = (\"IMAGE\",)\n RETURN_NAMES = (\"rotated_image\", \"rotated_mask\",)\n FUNCTION = \"rotate_and_pad_image\"\n CATEGORY = CATEGORY_NAME\n\n def rotate_and_pad_image(self, image, angle, counter_clockwise):\n if angle == 0 or angle == 360:\n return (image,)\n\n image = image_rotate_with_pad(image, counter_clockwise, angle)\n\n return (image,)\n \n\n#==============================================================================\nclass LoadInstantIdAdapter:\n def __init__(self):\n pass\n\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"ipadapter\": (folder_paths.get_filename_list(\"ipadapter\"), { \"tooltip\": \"The default folder where the adapter is searched for is: models/ipadapter.\" }),\n }\n }\n\n RETURN_TYPES = (\"INSTANTID_ADAPTER\", \"RESAMPLER\", )\n RETURN_NAMES = (\"InstantId_adapter\", \"resampler\",)\n FUNCTION = \"load_instantId_adapter\"\n CATEGORY = CATEGORY_NAME\n\n def load_instantId_adapter(self, ipadapter):\n ipadapter_path = folder_paths.get_full_path(\"ipadapter\", ipadapter)\n model = comfy.utils.load_torch_file(ipadapter_path, safe_load=True)\n instantId = InstantId(model['ip_adapter'])\n\n resampler = Resampler(\n dim=1280,\n depth=4,\n dim_head=64,\n heads=20,\n num_queries=16,\n embedding_dim=512,\n output_dim=2048,\n ff_mult=4\n )\n resampler.load_state_dict(model[\"image_proj\"])\n return (instantId, resampler)\n\n\n#==============================================================================\nclass InstantIdAdapterApply:\n def __init__(self):\n pass\n\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"model\": (\"MODEL\", ),\n \"instantId_adapter\": (\"INSTANTID_ADAPTER\", ),\n \"face_conditioning\": (\"FACE_CONDITIONING\", ),\n \"strength\": (\"FLOAT\", {\"default\": 0.8, \"min\": 0, \"step\": 0.1, \"max\": 10},),\n }\n }\n\n RETURN_TYPES = (\"MODEL\",)\n RETURN_NAMES = (\"model\",)\n FUNCTION = \"apply_instantId_adapter\"\n CATEGORY = CATEGORY_NAME\n\n def apply_instantId_adapter(self, model, instantId_adapter, face_conditioning, strength):\n if strength == 0: return (model,)\n\n instantId = instantId_adapter.to(comfy.model_management.get_torch_device())\n patch_kwargs = {\n \"instantId\": instantId,\n \"scale\": strength,\n \"cond\": face_conditioning,\n \"number\": 0\n }\n\n m = model.clone()\n\n for id in [4,5,7,8]:\n block_indices = range(2) if id in [4, 5] else range(10)\n for index in block_indices:\n set_model_patch_replace(m, patch_kwargs, (\"input\", id, index))\n patch_kwargs[\"number\"] += 1\n block_indices = range(2) if id in [3, 4, 5] else range(10)\n for index in block_indices:\n set_model_patch_replace(m, patch_kwargs, (\"output\", id, index))\n patch_kwargs[\"number\"] += 1\n for index in range(10):\n set_model_patch_replace(m, patch_kwargs, (\"middle\", 1, index))\n patch_kwargs[\"number\"] += 1\n\n return (m,)\n\n\n#==============================================================================\n# based on ControlNetApplyAdvance from ComfyUi/nodes.py\nclass ControlNetInstantIdApply:\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"positive\": (\"CONDITIONING\", ),\n \"negative\": (\"CONDITIONING\", ),\n \"face_conditioning\": (\"FACE_CONDITIONING\", ),\n \"control_net\": (\"CONTROL_NET\", ),\n \"image\": (\"IMAGE\", ),\n \"strength\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01})\n }\n }\n\n RETURN_TYPES = (\"CONDITIONING\", \"CONDITIONING\", )\n RETURN_NAMES = (\"positive\", \"negative\",)\n FUNCTION = \"apply_controlnet\"\n CATEGORY = CATEGORY_NAME\n\n def apply_controlnet(self, positive, negative, face_conditioning, control_net, image, strength):\n if strength == 0:\n return (positive, negative)\n\n control_hint = image.movedim(-1,1)\n cnets = {}\n\n out = []\n for conditioning, isPositive in zip([positive, negative], [True, False]):\n c = []\n for t in conditioning:\n d = t[1].copy()\n\n prev_cnet = d.get(\"control\", None)\n if prev_cnet in cnets:\n c_net = cnets[prev_cnet]\n else:\n c_net = control_net.copy().set_cond_hint(control_hint, strength)\n c_net.set_previous_controlnet(prev_cnet)\n cnets[prev_cnet] = c_net\n\n if isPositive:\n d[\"cross_attn_controlnet\"] = face_conditioning.to(comfy.model_management.intermediate_device())\n else :\n d[\"cross_attn_controlnet\"] = torch.zeros_like(face_conditioning).to(comfy.model_management.intermediate_device())\n d[\"control\"] = c_net\n d[\"control_apply_to_uncond\"] = False\n\n n = [t[0], d]\n c.append(n)\n out.append(c)\n return (out[0], out[1],)\n\n\n#==============================================================================\nclass InstantIdAndControlnetApply:\n def __init__(self):\n pass\n\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"model\": (\"MODEL\", ),\n \"ipadapter_path\": (folder_paths.get_filename_list(\"ipadapter\"), { \"tooltip\": \"The default folder where the adapter is searched for is: models/ipadapter.\" }),\n \"control_net_name\": (folder_paths.get_filename_list(\"controlnet\"), ),\n \"face_embed\": (\"FACE_EMBED\", ),\n \"control_image\": (\"IMAGE\", ),\n \"adapter_strength\": (\"FLOAT\", {\"default\": 0.5, \"min\": 0, \"step\": 0.1, \"max\": 10},),\n \"control_net_strength\": (\"FLOAT\", {\"default\": 0.7, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n \"positive\": (\"CONDITIONING\", ),\n \"negative\": (\"CONDITIONING\", )\n }\n }\n\n RETURN_TYPES = (\"MODEL\", \"CONDITIONING\", \"CONDITIONING\",)\n RETURN_NAMES = (\"model\", \"positive\", \"negative\",)\n FUNCTION = \"apply_instantId_adapter_and_controlnet\"\n CATEGORY = CATEGORY_NAME\n\n def apply_instantId_adapter_and_controlnet(\n self, model, ipadapter_path, control_net_name, face_embed, control_image,\n adapter_strength, control_net_strength, positive, negative\n ):\n graph = GraphBuilder()\n loadInstantIdAdapter = graph.node(\n \"LoadInstantIdAdapter\", ipadapter=ipadapter_path\n )\n faceEmbedCombine = graph.node(\n \"FaceEmbedCombine\", resampler=loadInstantIdAdapter.out(1), face_embeds=face_embed\n )\n loadControlNet = graph.node(\n \"ControlNetLoader\", control_net_name = control_net_name\n )\n instantIdApply = graph.node(\n \"InstantIdAdapterApply\", model=model, instantId_adapter=loadInstantIdAdapter.out(0),\n face_conditioning=faceEmbedCombine.out(0), strength=adapter_strength\n )\n controlNetInstantIdApply = graph.node(\n \"ControlNetInstantIdApply\", positive=positive, negative=negative,\n face_conditioning=faceEmbedCombine.out(0), control_net=loadControlNet.out(0),\n image=control_image, strength=control_net_strength\n )\n\n return {\n \"result\": (instantIdApply.out(0), controlNetInstantIdApply.out(0), controlNetInstantIdApply.out(1),),\n \"expand\":graph.finalize()\n }\n\n\n#==============================================================================\nclass PreprocessImageAdvanced:\n resize_modes = [\"auto\", \"free\", \"scale by width\", \"scale by height\"]\n upscale_methods = [\"nearest-exact\", \"bilinear\", \"area\", \"bicubic\", \"lanczos\"]\n\n def __init__(self):\n pass\n\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"image\": (\"IMAGE\", { \"tooltip\": \"Pose image.\" }),\n \"mask\": (\"MASK\",),\n \"width\": (\"INT\", {\"default\": 1024, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"height\": (\"INT\", {\"default\": 1024, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"resize_mode\": (self.resize_modes,),\n \"upscale_method\": (self.upscale_methods,),\n \"pad_top\": (\"INT\", {\"default\": 100, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"pad_right\": (\"INT\", {\"default\": 100, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"pad_bottom\": (\"INT\", {\"default\": 100, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"pad_left\": (\"INT\", {\"default\": 100, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n },\n \"optional\": {\n \"insightface\": (\"INSIGHTFACE_APP\",),\n }\n }\n\n RETURN_TYPES = (\"IMAGE\", \"MASK\", \"IMAGE\", \"INT\", \"INT\", \"INT\", \"INT\", \"INT\", \"INT\",)\n RETURN_NAMES = (\"resized_image\", \"mask\", \"control_image\", \"x\", \"y\", \"original_width\", \"original_height\", \"new_width\", \"new_height\",)\n FUNCTION = \"preprocess_image\"\n CATEGORY = CATEGORY_NAME\n\n def preprocess_image(\n self, image, mask, width, height, resize_mode, upscale_method,\n pad_top, pad_right, pad_bottom, pad_left, insightface = None\n ):\n\n p_x1, p_y1, p_x2, p_y2 = get_mask_bbox_with_padding(mask.squeeze(0), pad_top, pad_right, pad_bottom, pad_left)\n mask = mask[:, p_y1:p_y2, p_x1:p_x2]\n image = image[:, p_y1:p_y2, p_x1:p_x2]\n kps = get_kps_from_image(image, insightface) if insightface else None\n _, original_height, original_width, _ = image.shape\n\n if resize_mode == \"auto\":\n width, height = resize_to_fit_area(int(p_x2 - p_x1), int(p_y2 - p_y1), width, height)\n else:\n if resize_mode != \"free\":\n ratio = original_width / original_height\n if resize_mode == \"scale by width\":\n height = int(width / ratio)\n if resize_mode == \"scale by height\":\n width = int(height * ratio)\n\n width = (width // 8) * 8\n height = (height // 8) * 8\n\n mask = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)\n image = image.movedim(-1,1)\n mask = mask.movedim(-1,1)\n\n mask = comfy.utils.common_upscale(mask, width, height, \"bilinear\", \"disabled\")\n image = comfy.utils.common_upscale(image, width, height, upscale_method, \"disabled\")\n\n mask = mask.movedim(1,-1)\n mask = mask[:, :, :, 0]\n image = image.movedim(1,-1)\n _, new_height, new_width = mask.shape\n\n if kps is not None:\n kps *= [image.shape[2] / original_width, image.shape[1] / original_height]\n control_image = draw_kps(width, height, kps)\n control_image = (torch.from_numpy(control_image).float() / 255.0).unsqueeze(0)\n\n return (\n image, mask,\n control_image if kps is not None else None,\n p_x1, p_y1, original_width, original_height,\n new_width, new_height,\n )\n\n\n#==============================================================================\nclass PreprocessImage(PreprocessImageAdvanced):\n def __init__(self):\n pass\n\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"image\": (\"IMAGE\",),\n \"mask\": (\"MASK\",),\n \"width\": (\"INT\", {\"default\": 1024, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"height\": (\"INT\", {\"default\": 1024, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"resize_mode\": (self.resize_modes,),\n \"pad\": (\"INT\", {\"default\": 100, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n },\n \"optional\": {\n \"insightface\": (\"INSIGHTFACE_APP\",),\n }\n }\n\n FUNCTION = \"preprocess_image_simple\"\n CATEGORY = CATEGORY_NAME\n\n def preprocess_image_simple(self, image, mask, width, height, resize_mode, pad, insightface = None):\n return self.preprocess_image(\n image, mask, width, height, resize_mode, \"bilinear\", pad, pad, pad, pad, insightface\n )\n\n\n#==============================================================================\nclass LoadInsightface:\n def __init__(self):\n pass\n\n @classmethod\n def INPUT_TYPES(self):\n return {}\n\n RETURN_TYPES = (\"INSIGHTFACE_APP\",)\n RETURN_NAMES = (\"insightface\",)\n FUNCTION = \"load_insightface\"\n CATEGORY = CATEGORY_NAME\n\n def load_insightface(self):\n app = FaceAnalysis(\n name=\"antelopev2\",\n root=INSIGHTFACE_PATH,\n providers=[\"CPUExecutionProvider\", \"CUDAExecutionProvider\"]\n )\n app.prepare(ctx_id=0, det_size=(640, 640))\n return (app,)\n\n\n#==============================================================================\nclass KpsDraw:\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"width\": (\"INT\", {\"default\": 1024, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"height\": (\"INT\", {\"default\": 1024, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"kps\": (\"HIDDEN_STRING_JSON\", ),\n },\n \"optional\": {\n \"image_reference\": (\"IMAGE\",),\n }\n }\n\n RETURN_TYPES = (\"KPS_DATA\",)\n RETURN_NAMES = (\"kps_data\",)\n FUNCTION = \"draw_kps\"\n CATEGORY = CATEGORY_NAME\n\n def draw_kps(self, width, height, kps, image_reference = None):\n return (kps,)\n \n \n#==============================================================================\nclass Kps3dFromImage:\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"width\": (\"INT\", {\"default\": 1024, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"height\": (\"INT\", {\"default\": 1024, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"kps\": (\"HIDDEN_STRING_JSON\", ),\n },\n \"optional\": {\n \"image\": (\"IMAGE\",),\n }\n }\n\n RETURN_TYPES = (\"KPS_DATA_3D\", \"KPS_DATA\",)\n RETURN_NAMES = (\"kps_data_3d\", \"kps_data\")\n FUNCTION = \"make_kps\"\n CATEGORY = CATEGORY_NAME\n\n def make_kps(self, width, height, kps, image):\n\n kps_2d = kps3d_to_kps2d(json.loads(kps))\n\n return (kps, json.dumps(kps_2d),)\n\n\n#==============================================================================\nclass KpsMaker:\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"kps_data\": (\"KPS_DATA\",),\n }\n }\n\n RETURN_TYPES = (\"IMAGE\",)\n RETURN_NAMES = (\"control_image\",)\n FUNCTION = \"draw_kps\"\n CATEGORY = CATEGORY_NAME\n\n def draw_kps(self, kps_data):\n kps_data = json.loads(kps_data)\n\n control_image = draw_kps(kps_data['width'], kps_data[\"height\"], kps_data[\"array\"], alphas = kps_data[\"opacities\"])\n control_image = (torch.from_numpy(control_image).float() / 255.0).unsqueeze(0)\n\n return (control_image, )\n \n\n#==============================================================================\nclass Kps2dRandomizer:\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"kps_data\": (\"KPS_DATA\",),\n \"seed\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": 0xffffffffffffffff, \"tooltip\": \"The random seed used for randomizing KPS\"}),\n \"angle_min\": (\"INT\", {\"default\": 0, \"min\": -180, \"step\": 1, \"max\": 180}),\n \"angle_max\": (\"INT\", {\"default\": 0, \"min\": -180, \"step\": 1, \"max\": 180}),\n \"scale_min\": (\"FLOAT\", {\"default\": 1, \"min\": 0.1, \"step\": 0.01, \"max\": 5}),\n \"scale_max\": (\"FLOAT\", {\"default\": 1, \"min\": 0.1, \"step\": 0.01, \"max\": 5}),\n \"translate_x\": (\"INT\", {\"default\": 0, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"translate_y\": (\"INT\", {\"default\": 0, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n \"border\": (\"INT\", {\"default\": 0, \"min\": 0, \"step\": 1, \"max\": MAX_RESOLUTION}),\n }\n }\n\n RETURN_TYPES = (\"KPS_DATA\",)\n RETURN_NAMES = (\"kps_data\",)\n FUNCTION = \"rand_kps\"\n CATEGORY = CATEGORY_NAME\n\n def rand_kps(self, kps_data, seed, angle_min, angle_max, scale_min, scale_max, translate_x, translate_y, border):\n\n torch.manual_seed(seed)\n kps_data = json.loads(kps_data)\n\n angle = 0\n scale = 1\n width = kps_data['width']\n height = kps_data['height']\n\n # get random angle\n if angle_min != 0 and angle_max != 0:\n angle = torch.randint(angle_min, angle_max + 1, (1,)).item()\n\n # get random scale\n if scale_min != 1 and scale_max != 1:\n scale = (scale_max - scale_min) * torch.rand(1).item() + scale_min\n\n # get random translate_x and translate_y\n if translate_x != 0:\n random_translate_x = torch.randint(-int(translate_x), int(translate_x) + 1, (1,)).item()\n else:\n random_translate_x = 0\n\n if translate_y != 0:\n random_translate_y = torch.randint(-int(translate_y), int(translate_y) + 1, (1,)).item()\n else:\n random_translate_y = 0\n\n # rotate\n if angle != 0:\n centroid = np.mean(np.array(kps_data[\"array\"]), axis=0)\n angle_rad = np.radians(angle)\n\n rotated_points = []\n for x, y in kps_data[\"array\"]:\n translated_x = x - centroid[0]\n translated_y = y - centroid[1]\n \n rotated_x = translated_x * np.cos(angle_rad) - translated_y * np.sin(angle_rad)\n rotated_y = translated_x * np.sin(angle_rad) + translated_y * np.cos(angle_rad)\n\n rotated_points.append([rotated_x + centroid[0], rotated_y + centroid[1]])\n\n kps_data[\"array\"] = rotated_points\n \n # translate\n if random_translate_x != 0 or random_translate_y != 0:\n translated_points = []\n for x, y in kps_data[\"array\"]:\n translated_points.append([x + random_translate_x, y + random_translate_y])\n kps_data[\"array\"] = translated_points\n\n # scale\n if scale_min != 1 and scale_max != 1:\n scaled_points = []\n centroid = np.mean(np.array(kps_data[\"array\"]), axis=0)\n for x, y in kps_data[\"array\"]:\n scaled_points.append([\n centroid[0] + (x - centroid[0]) * scale,\n centroid[1] + (y - centroid[1]) * scale\n ])\n kps_data[\"array\"] = scaled_points\n\n # check border\n x_values = [x for x, _ in kps_data[\"array\"]]\n y_values = [y for _, y in kps_data[\"array\"]]\n\n min_x, max_x = min(x_values), max(x_values)\n min_y, max_y = min(y_values), max(y_values)\n\n shift_x = 0\n shift_y = 0\n\n if min_x < border:\n shift_x = border - min_x \n elif max_x > width - border:\n shift_x = (width - border) - max_x \n\n if min_y < border:\n shift_y = border - min_y \n elif max_y > height - border:\n shift_y = (height - border) - max_y \n\n\n\n final_output = []\n for x, y in kps_data[\"array\"]:\n shifted_x = x + shift_x\n shifted_y = y + shift_y\n final_output.append([int(shifted_x), int(shifted_y)])\n\n kps_data[\"array\"] = final_output\n\n return (json.dumps(kps_data), )\n\n#==============================================================================\nclass Kps3dRandomizer:\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"kps_data_3d\": (\"KPS_DATA_3D\",),\n \"seed\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": 0xffffffffffffffff, \"tooltip\": \"The random seed used for randomizing KPS\"}),\n \"rotate_x\": (\"INT\", {\"default\": 0, \"min\": -180, \"step\": 1, \"max\": 180}),\n \"rotate_y\": (\"INT\", {\"default\": 0, \"min\": -180, \"step\": 1, \"max\": 180}),\n \"rotate_z\": (\"INT\", {\"default\": 0, \"min\": -180, \"step\": 1, \"max\": 180})\n }\n }\n\n RETURN_TYPES = (\"KPS_DATA\",)\n RETURN_NAMES = (\"kps_data\",)\n FUNCTION = \"rand_kps\"\n CATEGORY = CATEGORY_NAME\n\n def rand_kps(self, kps_data_3d, seed, rotate_x, rotate_y, rotate_z):\n torch.manual_seed(seed)\n kps_data = json.loads(kps_data_3d)\n\n angle_x = 0\n if rotate_x != 0:\n angle_x = torch.randint(-rotate_x, rotate_x + 1, (1,)).item()\n angle_y = 0\n if rotate_y != 0:\n angle_y = torch.randint(-rotate_y, rotate_y + 1, (1,)).item()\n angle_z = 0\n if rotate_x != 0:\n angle_z = torch.randint(-rotate_z, rotate_z + 1, (1,)).item()\n\n angle_x += kps_data['rotateX']\n angle_y += kps_data['rotateY']\n angle_z += kps_data['rotateZ']\n if angle_x != 0 or angle_y != 0 or angle_z != 0: \n points = kps_rotate_3d(kps_data['array'], angle_x, angle_y, angle_z)\n else:\n points = kps_data['array']\n\n kps_data['array'] = points\n kps_data = kps3d_to_kps2d(kps_data)\n \n return (json.dumps(kps_data), )\n\n\n#==============================================================================\nclass Kps2dScaleBy:\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"kps_data\": (\"KPS_DATA\",),\n \"scale\": (\"FLOAT\", {\"default\": 1, \"min\": 0, \"max\": 100}),\n }\n }\n\n RETURN_TYPES = (\"KPS_DATA\",)\n RETURN_NAMES = (\"kps_data\",)\n FUNCTION = \"scale_kps_by\"\n CATEGORY = CATEGORY_NAME\n\n def scale_kps_by(self, kps_data, scale):\n kps_data = json.loads(kps_data)\n\n points = kps_data['array']\n kps_data['width'] = int(kps_data['width'] * scale)\n kps_data['height'] = int(kps_data['height'] * scale)\n for i, point in enumerate(points):\n kps_data['array'][i][0] = int(point[0] * scale)\n kps_data['array'][i][1] = int(point[1] * scale)\n \n return (json.dumps(kps_data), )\n\n\n#==============================================================================\nclass Kps2dScale:\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"kps_data\": (\"KPS_DATA\",),\n \"width\": (\"INT\", {\"default\": 1024, \"min\": 0, \"max\": MAX_RESOLUTION}),\n \"height\": (\"INT\", {\"default\": 1024, \"min\": 0, \"max\": MAX_RESOLUTION}),\n }\n }\n\n RETURN_TYPES = (\"KPS_DATA\",)\n RETURN_NAMES = (\"kps_data\",)\n FUNCTION = \"scale_kps\"\n CATEGORY = CATEGORY_NAME\n\n def scale_kps(self, kps_data, width, height):\n kps_data = json.loads(kps_data)\n\n points = kps_data['array']\n scaleX = width / kps_data['width']\n scaleY = height / kps_data['height']\n kps_data['width'] = int(kps_data['width'] * scaleX)\n kps_data['height'] = int(kps_data['height'] * scaleY)\n\n for i, point in enumerate(points):\n kps_data['array'][i][0] = int(point[0] * scaleX)\n kps_data['array'][i][1] = int(point[1] * scaleY)\n \n return (json.dumps(kps_data), )\n \n\n#==============================================================================\nclass Kps2dRotate:\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"kps_data\": (\"KPS_DATA\",),\n \"angle\": (\"FLOAT\", {\"default\": 0.0, \"min\": -360.0, \"step\": 0.1, \"max\": 360.0},),\n \"counter_clockwise\": (\"BOOLEAN\", {\"default\": True},),\n }\n }\n\n RETURN_TYPES = (\"KPS_DATA\",)\n RETURN_NAMES = (\"kps_data\",)\n FUNCTION = \"rotate_kps\"\n CATEGORY = CATEGORY_NAME\n\n def rotate_kps(self, kps_data, angle, counter_clockwise):\n if angle == 0 or angle == 360:\n return (kps_data,)\n\n if counter_clockwise: angle = -angle\n\n kps_data = json.loads(kps_data)\n\n points = kps_data['array']\n new_width, new_height = calculate_size_after_rotation(kps_data['width'], kps_data['height'], angle)\n\n kps_data['array'] = kps_rotate_2d(points, kps_data['width'], kps_data['height'], int(new_width), int(new_height), angle)\n kps_data['width'] = int(new_width)\n kps_data['height'] = int(new_height)\n\n return (json.dumps(kps_data), )\n \n#==============================================================================\nclass Kps2dCrop:\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"kps_data\": (\"KPS_DATA\",),\n \"x\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n \"y\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n \"width\": (\"INT\", {\"default\": 1024, \"min\": 1, \"max\": MAX_RESOLUTION, \"step\": 1}),\n \"height\": (\"INT\", {\"default\": 1024, \"min\": 1, \"max\": MAX_RESOLUTION, \"step\": 1}),\n }\n }\n\n RETURN_TYPES = (\"KPS_DATA\",)\n RETURN_NAMES = (\"kps_data\",)\n FUNCTION = \"crop_kps\"\n CATEGORY = CATEGORY_NAME\n\n def crop_kps(self, kps_data, x, y, width, height):\n kps_data = json.loads(kps_data)\n\n kps_data['width'] = width\n kps_data['height'] = height\n\n points = kps_data['array']\n\n for i, point in enumerate(points):\n kps_data['array'][i][0] = point[0] - x#\n kps_data['array'][i][1] = point[1] - y#\n\n return (json.dumps(kps_data), )\n \n\n#==============================================================================\nclass MaskFromKps:\n @classmethod\n def INPUT_TYPES(self):\n return {\n \"required\": {\n \"kps_data\": (\"KPS_DATA\",),\n \"grow_by\": (\"INT\", {\"default\": 4, \"min\": 1, \"max\": 10, \"step\": 1}),\n }\n }\n\n RETURN_TYPES = (\"MASK\",)\n RETURN_NAMES = (\"mask\",)\n FUNCTION = \"creat_mask\"\n CATEGORY = CATEGORY_NAME\n\n def creat_mask(self, kps_data, grow_by):\n kps_data = json.loads(kps_data)\n bbox = get_bbox_from_kps(kps_data, grow_by)\n mask = torch.zeros((kps_data['height'], kps_data['width']))\n mask[bbox[0][1]:bbox[1][1], bbox[0][0]:bbox[1][0]] = 1\n\n return (mask.unsqueeze(0), )\n\nNODE_CLASS_MAPPINGS = {\n \"LoadInsightface\": LoadInsightface,\n \"LoadInstantIdAdapter\": LoadInstantIdAdapter,\n \"InstantIdAdapterApply\": InstantIdAdapterApply,\n \"ControlNetInstantIdApply\": ControlNetInstantIdApply,\n \"InstantIdAndControlnetApply\": InstantIdAndControlnetApply,\n \"PreprocessImage\": PreprocessImage,\n \"PreprocessImageAdvanced\": PreprocessImageAdvanced,\n \"AngleFromFace\": AngleFromFace,\n \"AngleFromKps\": AngleFromKps,\n \"RotateImage\": RotateImage,\n \"ComposeRotated\": ComposeRotated,\n \"KpsDraw\": KpsDraw,\n \"Kps3dFromImage\": Kps3dFromImage,\n \"KpsMaker\": KpsMaker,\n \"Kps2dRandomizer\": Kps2dRandomizer,\n \"Kps3dRandomizer\": Kps3dRandomizer,\n \"KpsScale\": Kps2dScale,\n \"KpsScaleBy\": Kps2dScaleBy,\n \"KpsRotate\": Kps2dRotate,\n \"KpsCrop\": Kps2dCrop,\n \"MaskFromKps\": MaskFromKps,\n \"FaceEmbed\": FaceEmbed,\n \"FaceEmbedCombine\": FaceEmbedCombine\n\n}\n\nNODE_DISPLAY_NAME_MAPPINGS = {\n \"LoadInsightface\": \"Load insightface\",\n \"LoadInstantIdAdapter\": \"Load instantId adapter\",\n \"InstantIdAdapterApply\": \"Apply instantId adapter\",\n \"ControlNetInstantIdApply\": \"Apply instantId ControlNet\",\n \"InstantIdAndControlnetApply\": \"Apply instantId and ControlNet\",\n \"PreprocessImage\": \"Preprocess image for instantId\",\n \"PreprocessImagAdvancese\": \"Preprocess image for instantId (Advanced)\",\n \"AngleFromFace\": \"Get Angle from face\",\n \"AngleFromKps\": \"Get Angle from KPS data\",\n \"RotateImage\": \"Rotate Image\",\n \"ComposeRotated\": \"Remove rotation padding\",\n \"KpsDraw\": \"Draw KPS\",\n \"Kps3dFromImage\": \"3d KPS from image\",\n \"KpsMaker\": \"Create KPS Image\",\n \"Kps2dRandomizer\": \"Randomize 2d KPS\",\n \"Kps3dRandomizer\": \"Randomize 3d KPS\",\n \"Kps2dScaleBy\": \"Scale 2d KPS by\",\n \"Kps2dScale\": \"Scale 2d KPS\",\n \"KpsRotate\": \"Rotate 2d KPS\",\n \"KpsCrop\": \"Crop 2d KPS\",\n \"MaskFromKps\": \"Create mask from Kps\",\n \"FaceEmbed\": \"FaceEmbed for instantId\",\n \"FaceEmbedCombine\": \"FaceEmbed Combine\"\n}" }, { "path": "pyproject.toml", "content": "[project]\nname = \"comfyui-instantid-faceswap\"\ndescription = \"Implementation of [a/faceswap](https://github.com/nosiu/InstantID-faceswap/tree/main) based on [a/InstantID](https://github.com/InstantID/InstantID) for ComfyUI.\"\nversion = \"0.1.1\"\nlicense = { file = \"LICENSE.txt\" }\ndependencies = [\"insightface\", \"onnxruntime-gpu\"]\n\n[project.urls]\nRepository = \"https://github.com/nosiu/comfyui-instantId-faceswap\"\n\n[tool.comfy]\nPublisherId = \"nosiu\"\nDisplayName = \"comfyui-instantId-faceswap\"\nIcon = \"\"" }, { "path": "requirements.txt", "content": "insightface\nonnxruntime-gpu" }, { "path": "ui/dialogs.js", "content": "import { createShader, vertexShaderSrc, fragmentShaderSrc } from \"./shaders.js\"\nimport { getPointsCenter, drawKps, checkWebGlSupport, rotatePoints3D } from \"./helpers.js\"\nimport { createSlider, createButton, createRadiobox } from \"./uiHelpers.js\"\n\nclass KPSDialogBase {\n constructor(w, h, img = undefined) {\n this.isDragging = false\n this.draggedPointIndex = null\n this.mousedown_x = undefined\n this.mousedown_y = undefined\n this.mousedown_pan_x = undefined\n this.mousedown_pan_y = undefined\n this.pan_x = 0\n this.pan_y = 0\n this.cursorX = undefined\n this.cursorY = undefined\n this.zoom_ratio = 1\n this.min_zoom = undefined\n this.showOpacities = false\n if (img) {\n this.canvasWidth = img.width\n this.canvasHeight = img.height\n } else {\n this.canvasWidth = w\n this.canvasHeight = h\n }\n\n // ---------------------------------\n this.element = document.createElement(\"div\")\n this.element.style.display = \"none\"\n this.element.style.width = \"80vw\"\n this.element.style.height = \"80vh\"\n this.element.style.zIndex = 8888\n this.element.classList.add('comfy-modal')\n this.element.classList.add('kps-sandbox')\n\n document.body.appendChild(this.element)\n\n this.canvas = document.createElement(\"canvas\")\n this.canvas.style.position = \"absolute\"\n this.canvas.style.pointerEvents = \"auto\"\n this.canvas.style.zIndex = \"-1\"\n this.element.appendChild(this.canvas)\n\n this.canvas.width = this.canvasWidth\n this.canvas.height = this.canvasHeight\n }\n\n initializeCanvasPanZoom () {\n let drawWidth = this.canvasWidth;\n let drawHeight = this.canvasHeight;\n let width = this.element.clientWidth;\n let height = this.element.clientHeight;\n \n if (this.canvasWidth > width) {\n drawWidth = width;\n drawHeight = drawWidth / this.canvasWidth * this.canvasHeight;\n }\n if (drawHeight > height) {\n drawHeight = height;\n drawWidth = drawHeight / this.canvasHeight * this.canvasWidth;\n }\n this.zoom_ratio = drawWidth / this.canvasWidth;\n this.min_zoom = drawWidth / this.canvasWidth\n \n const canvasX = (width - drawWidth) / 2;\n const canvasY = (height - drawHeight) / 2;\n this.pan_x = canvasX;\n this.pan_y = canvasY;\n this.invalidatePanZoom();\n }\n \n invalidatePanZoom () {\n let raw_width = this.canvasWidth * this.zoom_ratio;\n let raw_height = this.canvasHeight * this.zoom_ratio;\n if (this.pan_x + raw_width < 10) {\n this.pan_x = 10 - raw_width;\n }\n if (this.pan_y + raw_height < 10) {\n this.pan_y = 10 - raw_height;\n }\n \n this.canvas.style.width = `${raw_width}px`;\n this.canvas.style.height = `${raw_height}px`;\n this.canvas.style.left = `${this.pan_x}px`;\n this.canvas.style.top = `${this.pan_y}px`;\n \n if (this.hasImage) {\n this.imageCanvas.style.width = `${raw_width}px`;\n this.imageCanvas.style.height = `${raw_height}px`;\n this.imageCanvas.style.left = `${this.pan_x}px`;\n this.imageCanvas.style.top = `${this.pan_y}px`;\n }\n }\n\n setBasicControls () {\n const buttonBar = document.createElement(\"div\")\n buttonBar.id = \"instantIdButtonBar\"\n buttonBar.style.position = \"absolute\"\n buttonBar.style.bottom = \"0\"\n buttonBar.style.height = \"50px\"\n buttonBar.style.left = \"20px\"\n buttonBar.style.right = \"20px\"\n buttonBar.style.pointerEvents = \"none\"\n buttonBar.appendChild(createButton(\"Save\", true, this.save.bind(this)))\n buttonBar.appendChild(createButton(\"Cancel\", true, this.closeModal.bind(this)))\n buttonBar.appendChild(createButton(\"Reset pan & zoom\", false, () => {\n this.initializeCanvasPanZoom();\n }));\n buttonBar.appendChild(createButton(\"Reset KPS\", false, () => {\n this.kps = this.getDefaultKps();\n this.draw();\n }));\n\n buttonBar.appendChild(this.createZoomSlider())\n \n this.element.appendChild(buttonBar);\n // ----------------------\n const opacitiesButton = createButton(\n \"Opacity options\", false, () => {\n this.showOpacities = !this.showOpacities\n if (this.showOpacities) {\n opacitiesButton.innerText = \"Hide options\"\n const advancedDiv = document.createElement(\"div\")\n advancedDiv.style.overflow = \"auto\"\n advancedDiv.id = \"kpsDialog0\"\n advancedDiv.style.padding = \"20px\"\n advancedDiv.style.paddingTop = \"50px\"\n advancedDiv.style.width = \"200px\"\n advancedDiv.style.height = \"100%\"\n advancedDiv.style.position = \"absolute\"\n advancedDiv.style.display = \"flex\"\n advancedDiv.style.left = \"0\"\n advancedDiv.style.top = \"0\"\n advancedDiv.style.backgroundColor = \"black\"\n advancedDiv.style.color = \"white\"\n \n const radioBar = document.createElement(\"div\");\n radioBar.style.marginTop = \"20px\"\n radioBar.style.pointerEvents = \"auto\"\n \n radioBar.appendChild(createRadiobox(\"red\", \"red opacity\", this.kpsOpacities, 0, this.draw.bind(this)))\n radioBar.appendChild(createRadiobox(\"green\", \"green opacity\", this.kpsOpacities, 1, this.draw.bind(this)))\n radioBar.appendChild(createRadiobox(\"blue\", \"blue opacity\", this.kpsOpacities, 2, this.draw.bind(this)))\n radioBar.appendChild(createRadiobox(\"yellow\", \"yellow opacity\", this.kpsOpacities, 3, this.draw.bind(this)))\n radioBar.appendChild(createRadiobox(\"purple\", \"purple opacity\", this.kpsOpacities, 4, this.draw.bind(this)))\n\n advancedDiv.appendChild(radioBar);\n this.element.appendChild(advancedDiv)\n } else {\n opacitiesButton.innerText = \"Opacity options\"\n const el = document.querySelector(\"#kpsDialog0\")\n if (el) el.remove()\n }\n }\n )\n opacitiesButton.style.zIndex = 8889\n opacitiesButton.style.position = \"absolute\"\n this.element.appendChild(opacitiesButton);\n }\n\n createZoomSlider () {\n const el = createSlider(\"Zoom\", \"instantIdZoomSlider\", this.min_zoom, \"2\", \"0.1\", this.zoom_ratio, (event) => {\n this.zoom_ratio = parseFloat(event.target.value)\n this.invalidatePanZoom()\n })\n return el\n }\n\n drawMoveAll () {\n const pad = 20\n const w = 60\n const ctx = this.canvas.getContext('2d')\n let x = this.kps.reduce((a, b) => a[0] > b[0] ? a : b)[0] + pad\n let y = this.kps.reduce((a, b) => a[1] > b[1] ? a : b)[1] + pad\n \n ctx.beginPath();\n ctx.fillStyle = \"rgb(8, 105, 216)\";\n \n ctx.beginPath();\n ctx.rect(x, y, w, w);\n ctx.fill();\n \n ctx.font = '40px Arial';\n ctx.fillStyle = 'white';\n ctx.textAlign = 'center';\n ctx.fillText('M', x + 30, y + 40);\n }\n}\n\nexport class KPSDialog2d extends KPSDialogBase{\n constructor(w, h, referenceImage, angleWidget, kpsJsonWidget) {\n super(w, h, referenceImage)\n\n this.hasImage = referenceImage ? true : false\n if (this.hasImage) {\n this.opacity = \"0.6\"\n } else {\n this.opacity = \"1\"\n }\n\n this.kpsOpacities = kpsJsonWidget.value.opacities.length ? JSON.parse(JSON.stringify(kpsJsonWidget.value.opacities)) : [1, 1, 1, 1, 1]\n this.kps = kpsJsonWidget.value.array.length ? JSON.parse(JSON.stringify(kpsJsonWidget.value.array)) : this.getDefaultKps()\n\n this.angleWidget = angleWidget\n this.kpsJsonWidget = kpsJsonWidget\n \n if (this.hasImage) {\n this.imageCanvas = document.createElement(\"canvas\")\n this.imageCanvas.style.position = \"absolute\"\n this.imageCanvas.style.zIndex = \"-2\"\n this.imageCanvas.style.pointerEvents = \"none\"\n this.element.appendChild(this.imageCanvas)\n this.imageCanvas.width = this.canvasWidth\n this.imageCanvas.height = this.canvasHeight\n }\n\n this.setBasicControls()\n this.setControls()\n this.attachListeners()\n this.canvas.style.opacity = this.opacity\n this.element.style.display = \"block\"\n this.initializeCanvasPanZoom()\n\n this.draw()\n if (this.hasImage) this.drawImage(referenceImage)\n }\n\n getDefaultKps () {\n const halfWidth = this.canvasWidth / 2;\n const halfHeight = this.canvasHeight / 2;\n return [\n [halfWidth - halfWidth / 2, halfHeight - halfHeight / 2],\n [halfWidth + halfWidth / 2, halfHeight - halfHeight / 2],\n [halfWidth, halfHeight],\n [halfWidth - halfWidth / 2, halfHeight + halfHeight / 2],\n [halfWidth + halfWidth / 2, halfHeight + halfHeight / 2],\n ]\n }\n\n setControls () {\n const buttonBar = document.querySelector(\"#instantIdButtonBar\")\n if (this.hasImage) {\n buttonBar.appendChild(this.createOpacitySlider())\n }\n }\n\n createOpacitySlider () {\n const el = createSlider(\"Opacity\", \"instantIdOpacitySlider\", \"0.1\", \"1\", \"0.1\", this.opacity, (event) => {\n this.opacity = event.target.value\n this.canvas.style.opacity = event.target.value\n })\n return el\n }\n\n attachListeners () {\n this.canvas.addEventListener('mousedown', this.mouseDown.bind(this))\n this.canvas.addEventListener('mousemove', this.mouseMove.bind(this))\n this.canvas.addEventListener('mouseup', this.mouseUp.bind(this))\n this.element.addEventListener('wheel', this.wheel.bind(this))\n this.element.addEventListener('DOMMouseScroll', (e) => e.preventDefault()) // thanks firefox.\n this.element.addEventListener('keydown', (event) => {\n if (event.key === \"Escape\") {\n this.closeModal()\n } else if (event.key === \"ENTER\") {\n this.save()\n }\n })\n }\n\n closeModal () {\n document.body.removeChild(this.element)\n }\n\n async save () {\n\n const minX = Math.min(...this.kps.map(e => e[0]))\n const maxX = Math.max(...this.kps.map(e => e[0]))\n\n const minY = Math.min(...this.kps.map(e => e[1]))\n const maxY = Math.max(...this.kps.map(e => e[1]))\n\n this.kpsJsonWidget.value = {\n array: this.kps,\n opacities: this.kpsOpacities,\n width: this.canvasWidth,\n height: this.canvasHeight,\n bbox: [\n [\n Math.max(Math.ceil(minX - ((maxX - minX) /3)), 0),\n Math.max(Math.ceil(minY - ((maxY - minY) /3)), 0)\n ],\n [\n Math.min(Math.ceil(maxX + ((maxX - minX) /3)), this.canvasWidth),\n Math.min(Math.ceil(maxY + ((maxY - minY) /3)), this.canvasHeight)\n ], \n ]\n }\n\n this.kpsJsonWidget.callback()\n const a = this.kps[0]\n const b = this.kps[1]\n let angle = Math.atan2(b[1] - a[1], b[0] - a[0]) * 180 / Math.PI\n\n this.angleWidget.value = angle\n this.closeModal()\n }\n\n changePointsPosition(closer = false, step = 10) {\n step /= this.zoom_ratio;\n const center = this.kps[2]\n\n const points = this.kps\n\n const magnitudes = points.map((point, index) => {\n if (index === 2) return Infinity; // Skip the center point (no magnitude calculation)\n const direction = [center[0] - point[0], center[1] - point[1]]\n return Math.sqrt(direction[0] * direction[0] + direction[1] * direction[1])\n });\n\n const allGreaterThan50 = magnitudes.every(mag => mag > 10)\n\n return points.map((point, index) => {\n if (index === 2) return point\n const direction = [center[0] - point[0], center[1] - point[1]]\n const magnitude = magnitudes[index]\n\n const scaleFactor = closer ? (magnitude - step) / magnitude : (magnitude + step) / magnitude;\n const scaledMoveVector = [direction[0] * scaleFactor, direction[1] * scaleFactor];\n\n if (closer) {\n if (allGreaterThan50) {\n return [\n center[0] - scaledMoveVector[0],\n center[1] - scaledMoveVector[1]\n ];\n }\n return point\n } else {\n return [\n center[0] - scaledMoveVector[0],\n center[1] - scaledMoveVector[1]\n ];\n }\n });\n }\n\n\n mouseDown (event) {\n event.preventDefault()\n const { offsetX: mouseX, offsetY: mouseY } = event\n\n if (event.ctrlKey) {\n if (event.buttons == 1) {\n this.mousedown_x = event.clientX\n this.mousedown_y = event.clientY\n this.mousedown_pan_x = this.pan_x\n this.mousedown_pan_y = this.pan_y\n }\n return;\n } else {\n let maxX = -Infinity\n let maxY = -Infinity\n\n this.kps.forEach((kp, idx) => {\n let [x, y] = kp;\n x *= this.zoom_ratio\n y *= this.zoom_ratio\n maxX = x > maxX ? x : maxX\n maxY = y > maxY ? y : maxY\n const distance = Math.sqrt((x - mouseX) ** 2 + (y - mouseY) ** 2)\n if (distance < 20 * (this.zoom_ratio)) {\n this.isDragging = true;\n this.draggedPointIndex = idx;\n return;\n }\n });\n maxX += 20 * this.zoom_ratio\n maxY += 20 * this.zoom_ratio\n if ((mouseX >= maxX) && (mouseY >= maxY) && (mouseX < maxX + 60 * this.zoom_ratio) && (mouseY < maxY + 60 * this.zoom_ratio)){\n this.mousedown_x = event.clientX;\n this.mousedown_y = event.clientY;\n this.isDragging = true;\n this.draggedPointIndex = -1;\n }\n }\n }\n\n mouseMove (event) {\n event.preventDefault();\n const { offsetX: mouseX, offsetY: mouseY } = event\n this.cursorX = event.pageX\n this.cursorY = event.pageY\n if (event.ctrlKey) {\n if (event.buttons == 1) {\n if (this.mousedown_x) {\n let deltaX = this.mousedown_x - event.clientX\n let deltaY = this.mousedown_y - event.clientY\n this.pan_x = this.mousedown_pan_x - deltaX\n this.pan_y = this.mousedown_pan_y - deltaY\n this.invalidatePanZoom()\n }\n }\n }\n if (this.isDragging) {\n const transformedX = (mouseX) / this.zoom_ratio\n const transformedY = (mouseY) / this.zoom_ratio\n if(this.draggedPointIndex !== null && this.draggedPointIndex > -1) {\n this.kps[this.draggedPointIndex] = [transformedX, transformedY]\n } else if (this.draggedPointIndex === -1) {\n let deltaX = this.mousedown_x - event.clientX\n let deltaY = this.mousedown_y - event.clientY\n this.mousedown_x = event.clientX\n this.mousedown_y = event.clientY\n\n this.kps.forEach(el => {\n el[0] -= deltaX / this.zoom_ratio\n el[1] -= deltaY / this.zoom_ratio\n })\n }\n this.draw()\n }\n }\n\n mouseUp (event) {\n event.preventDefault()\n this.mousedown_x = null\n this.mousedown_y = null\n this.isDragging = false\n this.draggedPointIndex = null\n }\n\n wheel (event) {\n event.preventDefault()\n if (event.ctrlKey) {\n if (event.deltaY < 0) {\n this.zoom_ratio = Math.min(2, this.zoom_ratio + 0.2)\n } else {\n this.zoom_ratio = Math.max(this.min_zoom, this.zoom_ratio - 0.2)\n }\n document.querySelector(\"#instantIdZoomSlider input\").value = `${this.zoom_ratio}`\n this.invalidatePanZoom();\n }\n else if (event.altKey) {\n this.kps = this.changePointsPosition(event.deltaY > 0)\n this.draw()\n }\n }\n\n draw () {\n this.drawKeyPoints()\n this.drawMoveAll()\n }\n\n\n drawKeyPoints (canvas = this.canvas) {\n drawKps(canvas, this.kps, this.kpsOpacities)\n }\n\n drawImage (ref_image) {\n /*\n webgl2 is used to render the reference background\n with masked images it is impossible to get pixel values\n */\n if (checkWebGlSupport()) {\n const gl = this.imageCanvas.getContext(\"webgl2\");\n this.drawImageWebGL2(gl, ref_image)\n return\n }\n const ctx = this.imageCanvas.getContext(\"2d\")\n ctx.drawImage(ref_image, 0, 0);\n }\n\n drawImageWebGL2 (gl, image) {\n const program = gl.createProgram()\n const vertexShader = createShader(gl, gl.VERTEX_SHADER, vertexShaderSrc)\n const fragmentShader = createShader(gl, gl.FRAGMENT_SHADER, fragmentShaderSrc)\n\n if (!vertexShader || !fragmentShader) {\n return;\n }\n\n gl.attachShader(program, vertexShader)\n gl.attachShader(program, fragmentShader)\n gl.linkProgram(program);\n\n if (!gl.getProgramParameter(program, gl.LINK_STATUS)) {\n console.error(gl.getProgramInfoLog(program))\n return;\n }\n\n gl.useProgram(program);\n\n const buffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, buffer);\n gl.bufferData(\n gl.ARRAY_BUFFER,\n new Float32Array([\n -1, 1, 0, 1,\n -1, -1, 0, 0,\n 1, 1, 1, 1,\n 1, -1, 1, 0,\n ]),\n gl.STATIC_DRAW\n )\n\n gl.vertexAttribPointer(0, 2, gl.FLOAT, false, 4 * 4, 0)\n gl.enableVertexAttribArray(0)\n gl.vertexAttribPointer(1, 2, gl.FLOAT, false, 4 * 4, 2 * 4)\n gl.enableVertexAttribArray(1)\n\n gl.pixelStorei(gl.UNPACK_FLIP_Y_WEBGL, true);\n gl.activeTexture(gl.TEXTURE0)\n gl.uniform1i(gl.getUniformLocation(program, 'uSampler'), 0)\n\n const texture = gl.createTexture()\n gl.bindTexture(gl.TEXTURE_2D, texture)\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGB, this.imageCanvas.width, this.imageCanvas.height, 0, gl.RGB, gl.UNSIGNED_BYTE, image)\n gl.generateMipmap(gl.TEXTURE_2D)\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4)\n }\n}\n\n\nexport class KPSDialog3d extends KPSDialogBase{\n constructor(w, h, angleWidget, kpsJsonWidget) {\n super(w, h)\n\n this.showLandmarks = false\n\n this.defaultKpsData = kpsJsonWidget.value.defaultKpsData\n\n this.kpsOpacities = kpsJsonWidget.value.opacities.length ? JSON.parse(JSON.stringify(kpsJsonWidget.value.opacities)) : [1, 1, 1, 1, 1]\n this.kps = JSON.parse(JSON.stringify(kpsJsonWidget.value.array))\n\n this.angleWidget = angleWidget\n this.kpsJsonWidget = kpsJsonWidget\n \n \n const landmarks = [\n 'jawline', 'eyebrow_left', 'eyebrow_right', 'nose_bridge', 'nose_lower',\n 'eye_left', 'eye_right', 'mouth_outer', 'mouth_inner'\n ]\n landmarks.forEach(el => {\n this[el] = kpsJsonWidget.value[el].length ? JSON.parse(JSON.stringify(kpsJsonWidget.value[el])) : []\n })\n\n this.rotateX = kpsJsonWidget.value.rotateX || 0\n this.rotateY = kpsJsonWidget.value.rotateY || 0\n this.rotateZ = kpsJsonWidget.value.rotateZ || 0\n\n this.setBasicControls()\n this.setControls()\n this.attachListeners()\n\n this.element.style.display = \"block\"\n this.initializeCanvasPanZoom()\n\n this.draw()\n if (this.hasImage) this.drawImage(referenceImage)\n }\n\n getDefaultKps () {\n try {\n const data = JSON.parse(this.defaultKpsData)\n const landmarks = [\n 'jawline', 'eyebrow_left', 'eyebrow_right', 'nose_bridge', 'nose_lower',\n 'eye_left', 'eye_right', 'mouth_outer', 'mouth_inner'\n ]\n landmarks.forEach(el => {\n this[el] = data[el]\n })\n\n return data.array\n } catch (e) {\n console.error(e)\n }\n }\n\n setControls () {\n const buttonBar = document.querySelector(\"#instantIdButtonBar\")\n buttonBar.appendChild(this.createRotationXSlider())\n buttonBar.appendChild(this.createRotationYSlider())\n buttonBar.appendChild(this.createRotationZSlider())\n\n this.element.appendChild(buttonBar);\n }\n\n createRotationXSlider () {\n const el = createSlider(\"rotate X\", \"instantIdRotateX\", \"0\", \"360\", \"1\", this.rotateX, (event) => {\n this.rotateX = parseFloat(event.target.value)\n this.draw()\n })\n return el\n }\n\n createRotationYSlider () {\n const el = createSlider(\"rotate Y\", \"instantIdRotateY\", \"0\", \"360\", \"1\", this.rotateY, (event) => {\n this.rotateY = parseFloat(event.target.value)\n this.draw()\n })\n return el\n }\n\n createRotationZSlider () {\n const el = createSlider(\"rotate Z\", \"instantIdRotateZ\", \"0\", \"360\", \"1\", this.rotateZ, (event) => {\n this.rotateZ = parseFloat(event.target.value)\n this.draw()\n })\n return el\n }\n\n attachListeners () {\n this.canvas.addEventListener('mousedown', this.mouseDown.bind(this))\n this.canvas.addEventListener('mousemove', this.mouseMove.bind(this))\n this.canvas.addEventListener('mouseup', this.mouseUp.bind(this))\n this.element.addEventListener('wheel', this.wheel.bind(this))\n this.element.addEventListener('DOMMouseScroll', (e) => e.preventDefault()) // thanks firefox.\n this.element.addEventListener('keydown', (event) => {\n if (event.key === \"Escape\") {\n this.closeModal()\n } else if (event.key === \"ENTER\") {\n this.save()\n }\n })\n }\n\n closeModal () {\n document.body.removeChild(this.element)\n }\n\n async save () {\n this.kpsJsonWidget.value = {\n array: this.kps,\n opacities: this.kpsOpacities.map(el => parseFloat(el)),\n width: this.canvasWidth,\n height: this.canvasHeight,\n jawline: this.jawline,\n eyebrow_left: this.eyebrow_left,\n eyebrow_right: this.eyebrow_right,\n nose_bridge: this.nose_bridge,\n nose_lower: this.nose_lower,\n eye_left: this.eye_left,\n eye_right: this.eye_right,\n mouth_outer: this.mouth_outer,\n mouth_inner: this.mouth_inner,\n rotateX: this.rotateX,\n rotateY: this.rotateY,\n rotateZ: this.rotateZ,\n defaultKpsData: this.defaultKpsData\n }\n \n this.kpsJsonWidget.callback()\n const a = this.kps[0]\n const b = this.kps[1]\n let angle = Math.atan2(b[1] - a[1], b[0] - a[0]) * 180 / Math.PI\n\n this.angleWidget.value = angle\n this.closeModal()\n }\n\n changePointsPosition(closer = false, step = 10) {\n step /= this.zoom_ratio\n\n const center = getPointsCenter([\n ...this.kps, ...this.jawline, ...this.eyebrow_left, ...this.eyebrow_right, ...this.nose_bridge,\n ...this.nose_lower,...this.eye_left, ...this.eye_right, ...this.mouth_outer, ...this.mouth_inner\n ])\n\n const points = [\n ...this.kps, ...this.jawline, ...this.eyebrow_left, ...this.eyebrow_right, ...this.nose_bridge,\n ...this.nose_lower,...this.eye_left, ...this.eye_right, ...this.mouth_outer, ...this.mouth_inner\n ]\n\n const magnitudes = points.map(point => {\n const direction = [center[0] - point[0], center[1] - point[1], center[2] - point[2]]\n return Math.sqrt(direction[0] * direction[0] + direction[1] * direction[1] + direction[2] * direction[2])\n })\n\n const allGreaterThan50 = magnitudes.every(mag => mag > 10)\n\n points.forEach((point, index) => {\n const direction = [center[0] - point[0], center[1] - point[1], center[2] - point[2]]\n const magnitude = magnitudes[index]\n const unitVector = [direction[0] / magnitude, direction[1] / magnitude, direction[2] / magnitude]\n\n const scaleFactor = magnitude / Math.max(...magnitudes)\n const adjustedStep = step * scaleFactor\n const moveVector = [unitVector[0] * adjustedStep, unitVector[1] * adjustedStep, unitVector[2] * adjustedStep]\n\n if (closer) {\n if (allGreaterThan50) {\n point[0] += moveVector[0]\n point[1] += moveVector[1]\n point[2] += moveVector[2]\n }\n } else {\n point[0] -= moveVector[0]\n point[1] -= moveVector[1]\n point[2] -= moveVector[2]\n }\n })\n }\n\n mouseDown (event) {\n event.preventDefault()\n const { offsetX: mouseX, offsetY: mouseY } = event\n\n if (event.ctrlKey) {\n if (event.buttons == 1) {\n this.mousedown_x = event.clientX\n this.mousedown_y = event.clientY\n this.mousedown_pan_x = this.pan_x\n this.mousedown_pan_y = this.pan_y\n }\n return;\n } else {\n let maxX = -Infinity\n let maxY = -Infinity\n\n this.kps.forEach((kp) => {\n let [x, y] = kp\n x *= this.zoom_ratio\n y *= this.zoom_ratio\n maxX = x > maxX ? x : maxX\n maxY = y > maxY ? y : maxY\n });\n maxX += 20 * this.zoom_ratio\n maxY += 20 * this.zoom_ratio\n if ((mouseX >= maxX) && (mouseY >= maxY) && (mouseX < maxX + 60 * this.zoom_ratio) && (mouseY < maxY + 60 * this.zoom_ratio)){\n this.mousedown_x = event.clientX\n this.mousedown_y = event.clientY\n this.isDragging = true;\n this.draggedPointIndex = -1\n }\n }\n }\n\n mouseMove (event) {\n event.preventDefault();\n this.cursorX = event.pageX\n this.cursorY = event.pageY\n if (event.ctrlKey) {\n if (event.buttons == 1) {\n if (this.mousedown_x) {\n let deltaX = this.mousedown_x - event.clientX\n let deltaY = this.mousedown_y - event.clientY\n this.pan_x = this.mousedown_pan_x - deltaX\n this.pan_y = this.mousedown_pan_y - deltaY\n this.invalidatePanZoom()\n }\n }\n }\n if (this.isDragging) {\n let deltaX = this.mousedown_x - event.clientX\n let deltaY = this.mousedown_y - event.clientY\n this.mousedown_x = event.clientX\n this.mousedown_y = event.clientY\n const points = [\n this.kps, this.jawline, this.eyebrow_left, this.eyebrow_right, this.nose_bridge,\n this.nose_lower, this.eye_left, this.eye_right, this.mouth_outer, this.mouth_inner\n ]\n points.forEach(p => {\n p.forEach(el => {\n el[0] -= deltaX / this.zoom_ratio\n el[1] -= deltaY / this.zoom_ratio\n })\n })\n this.draw()\n }\n }\n\n mouseUp (event) {\n event.preventDefault()\n this.mousedown_x = null\n this.mousedown_y = null\n this.isDragging = false\n this.draggedPointIndex = null\n }\n\n wheel (event) {\n event.preventDefault()\n if (event.ctrlKey) {\n if (event.deltaY < 0) {\n this.zoom_ratio = Math.min(2, this.zoom_ratio + 0.2)\n } else {\n this.zoom_ratio = Math.max(this.min_zoom, this.zoom_ratio - 0.2)\n }\n document.querySelector(\"#instantIdZoomSlider input\").value = `${this.zoom_ratio}`\n this.invalidatePanZoom();\n }\n else if (event.altKey) {\n this.changePointsPosition(event.deltaY > 0)\n this.draw()\n }\n }\n\n draw () {\n this.drawKeyPoints()\n const landmarks = [\n this.jawline, this.eyebrow_left, this.eyebrow_right, this.nose_bridge,\n this.nose_lower, this.eye_left, this.eye_right, this.mouth_inner, this.mouth_outer\n ]\n landmarks.forEach(points => { this.drawLandmarks(points) })\n this.drawMoveAll()\n }\n\n drawLandmarks (p, canvas = this.canvas) {\n if (p.length === 0) return\n const ctx = canvas.getContext('2d')\n const points = rotatePoints3D(p.map(el => [...el]), this.kps, this.rotateX, this.rotateY, this.rotateZ)\n\n ctx.beginPath()\n ctx.strokeStyle = \"white\"\n\n for (let i = 1; i < points.length; i++) {\n ctx.moveTo(points[i - 1][0], points[i - 1][1]);\n ctx.lineTo(points[i][0], points[i][1]);\n }\n ctx.stroke()\n }\n\n drawKeyPoints (canvas = this.canvas) {\n const kps = rotatePoints3D(this.kps.map(el => [...el]), this.kps, this.rotateX, this.rotateY, this.rotateZ)\n drawKps(canvas, kps, this.kpsOpacities)\n }\n}" }, { "path": "ui/extension.js", "content": "import { app } from \"../../scripts/app.js\";\nimport { api } from \"../../scripts/api.js\";\nimport { drawKps, normalizePoints, rotatePoints3D, getImgFromInput, getDefaultKpsData } from \"./helpers.js\"\nimport { KPSDialog2d, KPSDialog3d } from \"./dialogs.js\"\n\n\napp.registerExtension({\n getCustomWidgets(app) {\n return {\n HIDDEN_STRING_JSON(node, inputName, inputData) {\n const widget = {\n type: inputData[0],\n name: inputName,\n async serializeValue() {\n return JSON.stringify(widget.value)\n }\n }\n node.addCustomWidget(widget)\n return widget\n }\n }\n },\n name: \"ComfyUI.instantid-faceswap\",\n async beforeRegisterNodeDef(nodeType, nodeData, app) {\n if (nodeType.comfyClass == \"KpsDraw\" || nodeType.comfyClass == \"Kps3dFromImage\") {\n nodeType.prototype.showImage = function () {\n let w = this.widgets.find(w => w.name === \"width\").value\n let h = this.widgets.find(w => w.name === \"height\").value\n\n if (w > 0 && h > 0) {\n let kpsWidget = this.widgets.find(w => w.name === \"kps\")\n let kps = kpsWidget.value.array\n let kps_opacities = kpsWidget.value.opacities\n\n if (kps?.length === 0) {\n try {\n const parsed_kps = JSON.parse(kpsWidget.value)\n kps = parsed_kps.array\n if (parsed_kps.opacities && parsed_kps.opacities.length) {\n kps_opacities = parsed_kps.opacities\n }\n } catch(e) {\n console.log(e)\n return;\n }\n }\n if (kps) {\n const c = document.createElement(\"canvas\")\n c.width = w\n c.height = h\n if (kpsWidget.value.rotateX || kpsWidget.value.rotateY || kpsWidget.value.rotateZ) {\n kps = rotatePoints3D(kps.map(el => [...el]), kps, kpsWidget.value.rotateX, kpsWidget.value.rotateY, kpsWidget.value.rotateZ)\n }\n drawKps(c, kps, kps_opacities)\n const image = new Image()\n image.src = c.toDataURL()\n this.imgs = [image]\n this.setSizeForImage()\n app.graph.setDirtyCanvas(true)\n }\n }\n }\n\n const onNodeCreated = nodeType.prototype.onNodeCreated;\n nodeType.prototype.onNodeCreated = function() {\n const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined;\n this.kpsJsonWidget = this.widgets.find(w => w.name === \"kps\")\n this.kpsJsonWidget.callback = this.showImage.bind(this)\n if (this.kpsJsonWidget.value == null) {\n this.kpsJsonWidget.value = getDefaultKpsData()\n }\n\n requestAnimationFrame(() => {\n if (this.kpsJsonWidget.value?.array?.length) {\n this.showImage();\n }\n })\n\n const angleWidget = this.addWidget(\"string\", \"angle\", \"\", () => {})\n\n if (nodeType.comfyClass == \"Kps3dFromImage\") {\n\n const div = document.createElement(\"div\")\n div.style.fontSize = \"12px\"\n div.style.backgroundColor = \"#323334\"\n div.style.padding = \"8px\"\n div.innerText = \"\";\n \n this.addDOMWidget(\"info_text2\", \"\", div, {getMaxHeight: () => 50})\n \n const doMagic = this.addWidget(\"button\", \"getKPS\", \"\", () => {\n const inputNode = getImgFromInput(this.getInputNode(0))\n\n const reference_image = inputNode.imgs[inputNode.imageIndex || 0].currentSrc\n div.style.color = \"white\"\n div.innerText = \"Getting landmarks ...\"\n\n doMagic.disabled = true\n openDialogWidget.disabled = true\n api.fetchApi(\"/get_keypoints_for_instantId\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({image: reference_image})\n }).then(async (data) => {\n const json = await data.json()\n if (json.error) {\n throw Error(json.error)\n }\n const normalizedPoints = normalizePoints(\n [\n ...json.data.jawline,\n ...json.data.eyebrow_left,\n ...json.data.eyebrow_right,\n ...json.data.nose_bridge,\n ...json.data.nose_lower,\n ...json.data.eye_left,\n ...json.data.eye_right,\n ...json.data.mouth_outer,\n ...json.data.mouth_inner,\n ...json.data.kps\n ],\n this.widgets.find(w => w.name === \"width\").value, this.widgets.find(w => w.name === \"height\").value\n )\n\n this.kpsJsonWidget.value = getDefaultKpsData()\n\n this.kpsJsonWidget.value.jawline = normalizedPoints.slice(0, 17)\n this.kpsJsonWidget.value.eyebrow_left = normalizedPoints.slice(17, 22)\n this.kpsJsonWidget.value.eyebrow_right = normalizedPoints.slice(22, 27)\n this.kpsJsonWidget.value.nose_bridge = normalizedPoints.slice(27, 31)\n this.kpsJsonWidget.value.nose_lower = normalizedPoints.slice(31, 36)\n this.kpsJsonWidget.value.eye_left = normalizedPoints.slice(36, 42)\n this.kpsJsonWidget.value.eye_right = normalizedPoints.slice(42, 48)\n this.kpsJsonWidget.value.mouth_outer = normalizedPoints.slice(48, 60)\n this.kpsJsonWidget.value.mouth_inner = normalizedPoints.slice(60, 68)\n\n this.kpsJsonWidget.value.array = [\n normalizedPoints[normalizedPoints.length - 5],\n normalizedPoints[normalizedPoints.length - 4],\n normalizedPoints[normalizedPoints.length - 3],\n normalizedPoints[normalizedPoints.length - 2],\n normalizedPoints[normalizedPoints.length - 1]\n ]\n this.kpsJsonWidget.value.width = this.widgets.find(w => w.name === \"width\").value\n this.kpsJsonWidget.value.height = this.widgets.find(w => w.name === \"height\").value\n\n this.kpsJsonWidget.value.defaultKpsData = JSON.stringify(this.kpsJsonWidget.value) \n div.style.color = \"#08a85a\"\n div.innerText = \"Success\"\n\n this.showImage()\n\n }).catch(e => {\n div.style.color = \"#C70039\"\n div.innerText = \"ERROR\"\n div.innerText = e.message || \"ERROR\"\n console.log(e)\n }).finally(() => {\n doMagic.disabled = false\n openDialogWidget.disabled = false\n })\n })\n doMagic.label = \"Get Kps From Image\";\n }\n\n\n const openDialogWidget = this.addWidget(\"button\", \"drawbtn\", \"\", () => {\n let w = this.widgets.find(w => w.name === \"width\").value\n let h = this.widgets.find(w => w.name === \"height\").value\n let reference_image\n const inputNode = getImgFromInput(this.getInputNode(0))\n if (inputNode?.imgs?.length && nodeType.comfyClass != \"Kps3dFromImage\") {\n reference_image = inputNode.imgs[inputNode.imageIndex || 0]\n w = reference_image.width\n h = reference_image.height\n }\n\n if (w > 0 && h > 0) {\n if (nodeType.comfyClass == \"Kps3dFromImage\") {\n new KPSDialog3d(w, h, angleWidget, this.kpsJsonWidget)\n } else {\n new KPSDialog2d(\n w, h, reference_image, angleWidget, this.kpsJsonWidget\n )\n }\n }\n });\n\n const buttonText = nodeType.comfyClass === \"KpsDraw\" ? \"draw kps\" : \"change kps\"\n\n openDialogWidget.label = buttonText\n angleWidget.label = \"angle: \"\n angleWidget.value = \"none\"\n angleWidget.disabled = true\n }\n this.serialize = true\n\n const onConnectionsChange = nodeType.prototype.onConnectionsChange;\n nodeType.prototype.onConnectionsChange = function (side, slot, connect, link_info, output) {\n const r = onConnectionsChange?.apply(this, arguments);\n\n if (output.name === \"image_reference\" && nodeType.comfyClass == \"KpsDraw\") {\n const widthWidget = this.widgets.find(w => w.name === \"width\");\n const heightWidget = this.widgets.find(w => w.name === \"height\");\n const angleWidget = this.widgets.find(w => w.name === \"angle\");\n\n this.imgs = []\n if (output.link) {\n widthWidget.disabled = true\n heightWidget.disabled = true\n const inputNode = getImgFromInput(this.getInputNode(0))\n if (inputNode?.imgs?.length) {\n const reference_image = inputNode.imgs[inputNode.imageIndex || 0]\n widthWidget.value = reference_image.width\n heightWidget.value = reference_image.height\n }\n } else {\n widthWidget.disabled = false\n heightWidget.disabled = false\n }\n\n if (angleWidget) {\n angleWidget.value = \"none\"\n }\n }\n\n if (output.name === \"image\" && nodeType.comfyClass == \"Kps3dFromImage\") {\n this.imgs = []\n const getKPSWidget = this.widgets.find(w => w.name === \"getKPS\")\n getKPSWidget.disabled = !!!output.link\n }\n return r;\n }\n }\n }\n})" }, { "path": "ui/helpers.js", "content": "export const getPointsCenter = (points) => {\n let sumX = 0, sumY = 0, sumZ = 0;\n points.forEach(([x, y, z]) => {\n sumX += x;\n sumY += y;\n if (z != null) sumZ += z\n });\n\n const ret = [sumX / points.length, sumY / points.length]\n if (points[0].length > 2) ret.push(sumZ / points.length)\n return ret\n}\n\nexport const getPoinsMinMax = (points) => {\n let minX = points[0][0], maxX = points[0][0];\n let minY = points[0][1], maxY = points[0][1];\n points.forEach(([x, y]) => {\n if (x < minX) minX = x;\n if (x > maxX) maxX = x;\n if (y < minY) minY = y;\n if (y > maxY) maxY = y;\n });\n return { minX, maxX, minY, maxY };\n}\n\nexport const drawKps = (canvas, kps, opacities) => {\n const color_list = [\n `255, 0, 0,`,\n `0, 255, 0,`,\n `0, 0, 255,`,\n `255, 255, 0,`,\n `255, 0, 255,`\n ]\n\n const ctx = canvas.getContext(\"2d\")\n const stickWidth = 10;\n const limbSeq = [[0, 2], [1, 2], [3, 2], [4, 2]]\n\n ctx.clearRect(0, 0, canvas.width, canvas.height)\n ctx.fillStyle = \"black\"\n ctx.fillRect(0, 0, canvas.width, canvas.height)\n ctx.save()\n limbSeq.forEach((limb, idx) => {\n const kp1 = kps[limb[0]]\n const kp2 = kps[limb[1]]\n const color = `rgba( ${color_list[limb[0]]} ${0.6 * opacities[limb[0]]})`\n\n const x = [kp1[0], kp2[0]];\n const y = [kp1[1], kp2[1]];\n const length = Math.sqrt((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2)\n const angle = Math.atan2(y[1] - y[0], x[1] - x[0])\n\n const num_points = 20;\n const polygon = []\n\n const midX = (x[0] + x[1]) / 2\n const midY = (y[0] + y[1]) / 2\n\n for (let i = 0; i <= num_points; i++) {\n const theta = (i / num_points) * Math.PI * 2\n const dx = (length / 2) * Math.cos(theta);\n const dy = (stickWidth / 2) * Math.sin(theta);\n const rx = Math.cos(angle) * dx - Math.sin(angle) * dy + midX\n const ry = Math.sin(angle) * dx + Math.cos(angle) * dy + midY\n polygon.push([rx, ry]);\n }\n\n ctx.beginPath();\n ctx.moveTo(polygon[0][0], polygon[0][1])\n for (let i = 1; i < polygon.length; i++) {\n ctx.lineTo(polygon[i][0], polygon[i][1])\n }\n ctx.closePath();\n ctx.fillStyle = color;\n ctx.fill();\n })\n\n kps.forEach((kp, idx) => {\n const [x, y] = kp;\n const color = `rgba( ${color_list[idx]} ${opacities[idx]})`\n ctx.beginPath();\n ctx.arc(x, y, 10, 0, Math.PI * 2);\n ctx.fillStyle = color;\n ctx.fill();\n });\n ctx.restore();\n}\n\nexport const checkWebGlSupport = () => {\n const canvas = document.createElement(\"canvas\");\n const gl = canvas.getContext(\"webgl2\")\n return !!gl\n}\n\nexport const normalizePoints = (points, w, h) => {\n const minValues = [\n Math.min(...points.map(p => p[0])),\n Math.min(...points.map(p => p[1])),\n Math.min(...points.map(p => p[2]))\n ];\n\n const maxValues = [\n Math.max(...points.map(p => p[0])),\n Math.max(...points.map(p => p[1])),\n Math.max(...points.map(p => p[2]))\n ];\n\n const ranges = [\n maxValues[0] - minValues[0], \n maxValues[1] - minValues[1],\n maxValues[2] - minValues[2] \n ];\n\n const scaleX = w / ranges[0]\n const scaleY = h / ranges[1]\n\n const scaleFactor = Math.min(scaleX, scaleY);\n\n const normalizedPoints = points.map(point => [\n (point[0] - minValues[0]) * scaleFactor,\n (point[1] - minValues[1]) * scaleFactor,\n (point[2] - minValues[2]) * scaleFactor\n ]);\n\n const maxNormalizedValues = [\n Math.max(...normalizedPoints.map(p => p[0])),\n Math.max(...normalizedPoints.map(p => p[1])),\n Math.max(...normalizedPoints.map(p => p[2]))\n ];\n\n const centerOffset = [\n (w - maxNormalizedValues[0]) / 2,\n (h - maxNormalizedValues[1]) / 2,\n -maxNormalizedValues[2] / 2\n ];\n\n const centeredPoints = normalizedPoints.map(point => [\n point[0] + centerOffset[0],\n point[1] + centerOffset[1],\n point[2] + centerOffset[2]\n ]);\n\n return centeredPoints;\n}\n\nexport const rotatePoints3D = (points, kps, angleXDeg, angleYDeg, angleZDeg) => {\n const angleX = angleXDeg * (Math.PI / 180);\n const angleY = angleYDeg * (Math.PI / 180);\n const angleZ = angleZDeg * (Math.PI / 180);\n\n const numPoints = kps.length;\n const center = kps.reduce((acc, point) => {\n acc[0] += point[0]\n acc[1] += point[1]\n acc[2] += point[2]\n return acc;\n }, [0, 0, 0]).map(coord => coord / numPoints)\n\n const translatedPoints = points.map(point => [\n point[0] - center[0],\n point[1] - center[1],\n point[2] - center[2]\n ]);\n\n function rotateX(point, angle) {\n const cosTheta = Math.cos(angle);\n const sinTheta = Math.sin(angle);\n return [\n point[0],\n point[1] * cosTheta - point[2] * sinTheta,\n point[1] * sinTheta + point[2] * cosTheta\n ];\n }\n\n function rotateY(point, angle) {\n const cosTheta = Math.cos(angle);\n const sinTheta = Math.sin(angle);\n return [\n point[0] * cosTheta + point[2] * sinTheta,\n point[1],\n -point[0] * sinTheta + point[2] * cosTheta\n ];\n }\n\n function rotateZ(point, angle) {\n const cosTheta = Math.cos(angle);\n const sinTheta = Math.sin(angle);\n return [\n point[0] * cosTheta - point[1] * sinTheta,\n point[0] * sinTheta + point[1] * cosTheta,\n point[2]\n ];\n }\n\n const rotatedPoints = translatedPoints.map(point => {\n let rotatedPoint = rotateX(point, angleX)\n rotatedPoint = rotateY(rotatedPoint, angleY)\n rotatedPoint = rotateZ(rotatedPoint, angleZ)\n return rotatedPoint;\n });\n\n const finalPoints = rotatedPoints.map(point => [\n point[0] + center[0],\n point[1] + center[1],\n point[2] + center[2]\n ])\n\n return finalPoints\n}\n\nexport const getImgFromInput = (inputNode) => {\n if (inputNode?.type === \"Reroute\") {\n return getImgFromInput(inputNode.getInputNode(0))\n }\n return inputNode\n} \n\nexport const getDefaultKpsData = () => ({\n array: [], height: 0, width: 0, rotateX: 0, rotateY: 0, rotateZ: 0 , opacities: [1, 1, 1, 1, 1],\n jawline: [], eyebrow_left: [], eyebrow_right: [], nose_bridge: [], nose_lower: [], \n eye_left: [], eye_right: [], mouth_outer: [], mouth_inner: []\n})" }, { "path": "ui/shaders.js", "content": "export const vertexShaderSrc = `#version 300 es\n#pragma vscode_glsllint_stage: vert\nlayout(location=0) in vec4 aPosition;\nlayout(location=1) in vec2 aTexCoord;\nout vec2 vTexCoord;\nvoid main()\n{\n vTexCoord = aTexCoord;\n gl_Position = aPosition;\n}`;\n\nexport const fragmentShaderSrc = `#version 300 es\n#pragma vscode_glsllint_stage: frag\nprecision mediump float;\nin vec2 vTexCoord;\nuniform sampler2D uSampler;\nout vec4 fragColor;\nvoid main()\n{\n fragColor = texture(uSampler, vTexCoord);\n}`;\n\n\nexport const createShader = (gl, type, source) => {\n const shader = gl.createShader(type)\n gl.shaderSource(shader, source)\n gl.compileShader(shader)\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n console.error(gl.getShaderInfoLog(shader))\n gl.deleteShader(shader)\n return null\n }\n return shader\n}" }, { "path": "ui/uiHelpers.js", "content": "export const createSlider = (name, id, min, max, step, value, callback) => {\n const divElement = document.createElement(\"div\");\n divElement.id = id;\n divElement.style.cssFloat = \"left\"\n divElement.style.fontFamily = \"sans-serif\"\n divElement.style.marginRight = \"4px\"\n divElement.style.color = \"var(--input-text)\"\n divElement.style.backgroundColor = \"var(--comfy-input-bg)\"\n divElement.style.borderRadius = \"8px\"\n divElement.style.borderColor = \"var(--border-color)\"\n divElement.style.borderStyle = \"solid\"\n divElement.style.fontSize = \"15px\"\n divElement.style.height = \"21px\"\n divElement.style.padding = \"1px 6px\"\n divElement.style.display = \"flex\"\n divElement.style.position = \"relative\"\n divElement.style.top = \"2px\"\n divElement.style.pointerEvents = \"auto\"\n\n const input = document.createElement(\"input\")\n const labelElement = document.createElement(\"label\")\n input.setAttribute(\"type\", \"range\")\n input.setAttribute(\"min\", `${min}`)\n input.setAttribute(\"max\", `${max}`)\n input.setAttribute(\"step\", `${step}`)\n input.setAttribute(\"value\", `${value}`)\n labelElement.textContent = name;\n divElement.appendChild(labelElement)\n divElement.appendChild(input)\n input.addEventListener(\"input\", callback)\n return divElement;\n }\n\n export const createButton = (name, isRight, callback) => {\n const button = document.createElement(\"button\");\n button.innerText = name;\n button.style.pointerEvents = \"auto\";\n button.addEventListener(\"click\", callback);\n if (isRight) {\n button.style.cssFloat = \"right\";\n button.style.marginLeft = \"4px\";\n } else {\n button.style.cssFloat = \"left\";\n button.style.marginRight = \"4px\";\n }\n return button;\n }\n\n export const createRadiobox = (name, label, opacities, index, callback) => {\n const div = document.createElement(\"div\");\n div.style.marginTop = \"20px\"\n const sliderInput = document.createElement(\"input\")\n sliderInput.style.pointerEvents = \"auto\"\n sliderInput.id = `opacity_slider_${name}`\n sliderInput.type = \"range\"\n sliderInput.step = \"0.1\"\n sliderInput.min = \"0\"\n sliderInput.max = \"1\"\n sliderInput.tabIndex = \"1\"\n sliderInput.style.width = \"100%\"\n sliderInput.name = `s_${name}`\n sliderInput.value = opacities[index]\n sliderInput.addEventListener(\"change\", (event) => {\n const input = document.querySelector(`#opacity_input_${name}`)\n if (input) input.value = event.target.value;\n opacities[index] = event.target.value;\n callback()\n })\n\n const valueInput = document.createElement(\"input\")\n valueInput.style.pointerEvents = \"auto\"\n valueInput.id = `opacity_input_${name}`\n valueInput.type = \"number\";\n valueInput.min = \"0\";\n valueInput.max = \"1\";\n valueInput.tabIndex = \"1\"\n valueInput.style.width = \"100%\"\n valueInput.name = `i_${name}`\n valueInput.value = opacities[index];\n valueInput.addEventListener(\"change\", (event) => {\n const input = document.querySelector(`#opacity_slider_${name}`)\n if (input) input.value = event.target.value\n opacities[index] = event.target.value\n callback()\n })\n\n div.style.marginRight = \"4px\";\n const labelDiv = document.createElement(\"div\")\n labelDiv.innerText = label\n div.appendChild(labelDiv)\n div.appendChild(sliderInput)\n div.appendChild(valueInput)\n return div\n }" }, { "path": "utils.py", "content": "import numpy as np\nimport cv2\nimport math\nimport torch\nimport math\nimport torch.nn.functional as F\nfrom torchvision.transforms import functional as TF\nfrom .ip_adapter.instantId import CrossAttentionPatch\n\ndef draw_kps(w, h, kps, color_list=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255)], alphas=[1, 1, 1, 1, 1]):\n stickwidth = 4\n limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])\n kps = np.array(kps)\n out_img = np.zeros([int(h), int(w), 3], dtype=np.uint8)\n\n for i in range(len(limbSeq)):\n index = limbSeq[i]\n color = color_list[index[0]]\n alpha = alphas[index[0]]\n\n x = kps[index][:, 0]\n y = kps[index][:, 1]\n length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5\n angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))\n\n polygon = cv2.ellipse2Poly(\n (int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1\n )\n\n limb_img = np.zeros_like(out_img)\n cv2.fillConvexPoly(limb_img, polygon, color)\n out_img = cv2.addWeighted(out_img, 1, limb_img, float(alpha) * 0.6, 0)\n\n for idx_kp, kp in enumerate(kps):\n color = color_list[idx_kp]\n alpha = alphas[idx_kp] \n x = kp[0]\n y = kp[1]\n kp_img = out_img.copy()\n cv2.circle(kp_img, (int(x), int(y)), 10, color, -1)\n out_img = cv2.addWeighted(out_img, 1 - float(alpha), kp_img, float(alpha), 0)\n\n return out_img.astype(np.uint8)\n\n\n# based on https://github.com/laksjdjf/IPAdapter-ComfyUI/blob/main/ip_adapter.py#L19\ndef set_model_patch_replace(model, patch_kwargs, key):\n attn = \"attn2\"\n to = model.model_options[\"transformer_options\"].copy()\n if \"patches_replace\" not in to:\n to[\"patches_replace\"] = {}\n else:\n to[\"patches_replace\"] = to[\"patches_replace\"].copy()\n\n if attn not in to[\"patches_replace\"]:\n to[\"patches_replace\"][attn] = {}\n else:\n to[\"patches_replace\"][attn] = to[\"patches_replace\"][attn].copy()\n if key not in to[\"patches_replace\"][attn]:\n to[\"patches_replace\"][attn][key] = CrossAttentionPatch(**patch_kwargs)\n model.model_options[\"transformer_options\"] = to\n else:\n to[\"patches_replace\"][attn][key].set_new_condition(**patch_kwargs)\n\n\ndef resize_to_fit_area(original_width, original_height, area_width, area_height):\n base_pixels = 8\n max_area= area_width * area_height\n aspect_ratio = original_width / original_height\n\n scale_factor = math.sqrt(max_area / (original_width * original_height))\n new_width = int(original_width * scale_factor)\n new_height = int(original_height * scale_factor)\n\n new_width = new_width // base_pixels * base_pixels\n new_height = new_height // base_pixels * base_pixels\n\n if new_width * new_height > max_area:\n new_width = math.floor(math.sqrt(max_area * aspect_ratio)) // base_pixels * base_pixels\n new_height = math.floor(new_width / aspect_ratio) // base_pixels * base_pixels\n\n return (new_width, new_height)\n\n\ndef get_mask_bbox_with_padding(mask_image, pad_top, pad_right, pad_bottom, pad_left):\n mask_segments = torch.nonzero(mask_image == 1, as_tuple=False)\n if torch.count_nonzero(mask_segments).item() == 0:\n raise Exception(\"Draw a mask on pose image\")\n\n m_y1 = torch.min(mask_segments[:, 0]).item()\n m_y2 = torch.max(mask_segments[:, 0]).item()\n m_x1 = torch.min(mask_segments[:, 1]).item()\n m_x2 = torch.max(mask_segments[:, 1]).item()\n\n height, width = mask_image.shape\n\n p_x1 = max(0, m_x1 - pad_left)\n p_y1 = max(0, m_y1 - pad_top)\n p_x2 = min(width, m_x2 + pad_right)\n p_y2 = min(height, m_y2 + pad_bottom)\n\n return int(p_x1), int(p_y1), int(p_x2), int(p_y2)\n\n\ndef get_kps_from_image(image, insightface):\n np_pose_image = (255.0 * image.cpu().numpy().squeeze()).clip(0, 255).astype(np.uint8)\n face_info = insightface.get(cv2.cvtColor(np_pose_image, cv2.COLOR_RGB2BGR))\n assert len(face_info) > 0, \"No face detected in pose image\"\n face_info = sorted(face_info, key=lambda x: (x[\"bbox\"][2] - x[\"bbox\"][0]) * (x[\"bbox\"][3] - x[\"bbox\"][1]))[-1] # only use the maximum face\n return face_info[\"kps\"]\n\n\ndef get_angle(a=(0, 0), b=(0, 0), round_angle=False):\n # a, b - eyes\n angle = math.atan2(b[1] - a[1], b[0] - a[0]) * 180 / math.pi\n if round_angle:\n angle = round(angle / 90) * 90\n if angle == 360: angle = 0\n\n return angle\n\n\ndef calculate_size_after_rotation(width, height, angle):\n angle_rad = math.radians(angle)\n \n new_width = abs(width * math.cos(angle_rad)) + abs(height * math.sin(angle_rad))\n new_height = abs(width * math.sin(angle_rad)) + abs(height * math.cos(angle_rad))\n \n return (int(np.ceil(new_width)), int(np.ceil(new_height))) #+ 1?\n\n\ndef image_rotate_with_pad(image, clockwise, angle):\n if not clockwise: angle *= -1\n\n image = image.squeeze(0)\n image = image.permute(2, 0, 1)\n image = TF.rotate(image, angle, fill=0, expand=True)\n image = image.permute(1, 2, 0)\n image = image.unsqueeze(0)\n return image\n\n\ndef kps_rotate_2d(points, original_width, original_height, new_width, new_height, angle):\n angle_rad = math.radians(angle)\n \n original_center_x = original_width / 2\n original_center_y = original_height / 2\n \n new_center_x = new_width / 2\n new_center_y = new_height / 2\n \n cos_angle = math.cos(angle_rad)\n sin_angle = math.sin(angle_rad)\n \n rotated_points = []\n \n for point in points:\n x, y = point\n \n translated_x = x - original_center_x\n translated_y = y - original_center_y\n \n rotated_x = translated_x * cos_angle - translated_y * sin_angle\n rotated_y = translated_x * sin_angle + translated_y * cos_angle\n \n final_x = int(round(rotated_x + new_center_x))\n final_y = int(round(rotated_y + new_center_y))\n \n rotated_points.append([final_x, final_y])\n \n return rotated_points\n\n\ndef kps_rotate_3d(points, angleXDeg, angleYDeg, angleZDeg):\n angleX = math.radians(angleXDeg)\n angleY = math.radians(angleYDeg)\n angleZ = math.radians(angleZDeg)\n\n center = np.mean(points, axis=0)\n\n translated_points = np.array([point - center for point in points])\n\n def rotate_x(point, angle):\n cos_theta = math.cos(angle)\n sin_theta = math.sin(angle)\n return [\n int(point[0]),\n int(point[1] * cos_theta - point[2] * sin_theta),\n int(point[1] * sin_theta + point[2] * cos_theta)\n ]\n\n def rotate_y(point, angle):\n cos_theta = math.cos(angle)\n sin_theta = math.sin(angle)\n return [\n int(point[0] * cos_theta + point[2] * sin_theta),\n int(point[1]),\n int(-point[0] * sin_theta + point[2] * cos_theta)\n ]\n\n def rotate_z(point, angle):\n cos_theta = math.cos(angle)\n sin_theta = math.sin(angle)\n return [\n int(point[0] * cos_theta - point[1] * sin_theta),\n int(point[0] * sin_theta + point[1] * cos_theta),\n int(point[2])\n ]\n\n rotated_points = [\n rotate_z(rotate_y(rotate_x(point, angleX), angleY), angleZ)\n for point in translated_points\n ]\n\n return [point + center for point in rotated_points]\n\n\ndef kps3d_to_kps2d (kps):\n if len(kps['array'][0]) == 3:\n kps2d = {\n 'width': kps['width'],\n 'height': kps['height'],\n 'opacities': kps['opacities'][:],\n 'array': []\n }\n\n for x, y, _ in kps['array']:\n kps2d['array'].append([x, y])\n\n return kps2d\n return kps\n\n\ndef get_bbox_from_kps (kps_data, grow_by):\n kps = np.array(kps_data['array'])\n minX, minY = np.min(kps, axis=0)\n maxX, maxY = np.max(kps, axis=0)\n width = (maxX - minX) / grow_by\n height = ((maxY - minY) / grow_by)\n\n return [\n [\n int(max(np.ceil(minX - (width)), 0)),\n int(max(np.ceil(minY - (height)), 0))\n ],\n [\n int(min(np.ceil(maxX + (width)), kps_data['width'])),\n int(min(np.ceil(maxY + (height)), kps_data['height']))\n ] \n ]" }, { "path": "workflows/auto_rotate.json", "content": "{\n \"last_node_id\": 682,\n \"last_link_id\": 1593,\n \"nodes\": [\n {\n \"id\": 268,\n \"type\": \"PreviewImage\",\n \"pos\": [\n 1260,\n -340\n ],\n \"size\": {\n \"0\": 210,\n \"1\": 246\n },\n \"flags\": {},\n \"order\": 35,\n \"mode\": 0,\n \"inputs\": [\n {\n \"name\": \"images\",\n \"type\": \"IMAGE\",\n \"link\": 926\n }\n ],\n \"title\": \"Image for inpaint\",\n \"properties\": {\n \"Node name for S&R\": \"PreviewImage\"\n },\n \"color\": \"#233\",\n \"bgcolor\": \"#355\"\n },\n {\n \"id\": 412,\n \"type\": \"Reroute\",\n \"pos\": [\n 1201,\n 144\n ],\n \"size\": [\n 75,\n 26\n ],\n \"flags\": {},\n \"order\": 32,\n \"mode\": 0,\n \"inputs\": [\n {\n \"name\": \"\",\n \"type\": \"*\",\n \"link\": 1475,\n \"widget\": {\n \"name\": \"value\"\n }\n }\n ],\n \"outputs\": [\n {\n \"name\": \"\",\n \"type\": \"INT\",\n \"links\": [\n 840\n ],\n \"slot_index\": 0\n }\n ],\n \"properties\": {\n \"showOutputText\": false,\n \"horizontal\": false\n }\n },\n {\n \"id\": 413,\n \"type\": \"Reroute\",\n \"pos\": [\n 1201,\n 164\n ],\n \"size\": [\n 75,\n 26\n ],\n \"flags\": {},\n \"order\": 33,\n \"mode\": 0,\n \"inputs\": [\n {\n \"name\": \"\",\n \"type\": \"*\",\n \"link\": 1476,\n \"widget\": {\n \"name\": \"value\"\n }\n }\n ],\n \"outputs\": [\n {\n \"name\": \"\",\n \"type\": \"INT\",\n \"links\": [\n 841\n ],\n \"slot_index\": 0\n }\n ],\n \"properties\": {\n \"showOutputText\": false,\n \"horizontal\": false\n }\n },\n {\n \"id\": 395,\n \"type\": \"PreviewImage\",\n \"pos\": [\n 1440,\n 430\n ],\n \"size\": {\n \"0\": 612.2093505859375,\n \"1\": 842.1597900390625\n },\n \"flags\": {},\n \"order\": 50,\n \"mode\": 0,\n \"inputs\": [\n {\n \"name\": \"images\",\n \"type\": \"IMAGE\",\n \"link\": 1535\n }\n ],\n \"title\": \"Output Image\",\n \"properties\": {\n \"Node name for S&R\": \"PreviewImage\"\n },\n \"color\": \"#233\",\n \"bgcolor\": \"#355\"\n },\n {\n \"id\": 630,\n \"type\": \"SetLatentNoiseMask\",\n \"pos\": [\n 894,\n 510\n ],\n \"size\": {\n \"0\": 210,\n \"1\": 46\n },\n \"flags\": {\n \"collapsed\": true\n },\n \"order\": 43,\n \"mode\": 0,\n \"inputs\": [\n {\n 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