Model Overview

This bundle is for Nvidia MAISI (Medical AI for Synthetic Imaging), a 3D Latent Diffusion Model that can generate large CT images with paired segmentation masks, variable volume size and voxel size, as well as controllable organ/tumor size.

The inference workflow of MAISI is depicted in the figure below. It first generates latent features from random noise by applying multiple denoising steps using the trained diffusion model. Then it decodes the denoised latent features into images using the trained autoencoder.

\n $\"MAISI$ \n

MAISI is based on the following papers:

Latent Diffusion: Rombach, Robin, et al. \"High-resolution image synthesis with latent diffusion models.\" CVPR 2022.

ControlNet: Lvmin Zhang, Anyi Rao, Maneesh Agrawala; “Adding Conditional Control to Text-to-Image Diffusion Models.” ICCV 2023.

Example synthetic image

An example result from inference is shown below:\n $\"Example$

Inference configuration

The inference requires:

GPU: at least 58GB GPU memory for 512 x 512 x 512
Disk Memory: at least 21GB disk memory

Inference parameters:

The information for the inference input, like body region and anatomy to generate, is stored in ./configs/inference.json. Please feel free to play with it. Here are the details of the parameters.

\"num_output_samples\": int, the number of output image/mask pairs it will generate.
\"spacing\": voxel size of generated images. E.g., if set to [1.5, 1.5, 2.0], it will generate images with a resolution of 1.5×1.5×2.0 mm. The spacing for x and y axes has to be between 0.5 and 3.0 mm and the spacing for the z axis has to be between 0.5 and 5.0 mm.
\"output_size\": volume size of generated images. E.g., if set to [512, 512, 256], it will generate images with size of 512×512×256. They need to be divisible by 16. If you have a small GPU memory size, you should adjust it to small numbers. Note that \"spacing\" and \"output_size\" together decide the output field of view (FOV). For eample, if set them to [1.5, 1.5, 2.0]mm and [512, 512, 256], the FOV is 768×768×512 mm. We recommend output_size is the FOV in x and y axis are same and to be at least 256mm for head, and at least 384mm for other body regions like abdomen. The output size for the x and y axes can be selected from [256, 384, 512], while for the z axis, it can be chosen from [128, 256, 384, 512, 640, 768].
\"controllable_anatomy_size\": a list of controllable anatomy and its size scale (0--1). E.g., if set to [[\"liver\", 0.5],[\"hepatic tumor\", 0.3]], the generated image will contain liver that have a median size, with size around 50% percentile, and hepatic tumor that is relatively small, with around 30% percentile. In addition, if the size scale is set to -1, it indicates that the organ does not exist or should be removed. The output will contain paired image and segmentation mask for the controllable anatomy.\nThe following organs support generation with a controllable size: [\"liver\", \"gallbladder\", \"stomach\", \"pancreas\", \"colon\", \"lung tumor\", \"bone lesion\", \"hepatic tumor\", \"colon cancer primaries\", \"pancreatic tumor\"].\nThe raw output of the current mask generation model has a fixed size of $256^3$ voxels with a spacing of $1.5^3$ mm. If the \"output_size\" differs from this default, the generated masks will be resampled to the desired \"output_size\" and \"spacing\". Note that resampling may degrade the quality of the generated masks and could trigger multiple inference attempts if the images fail to pass the image quality check.
\"body_region\": If \"controllable_anatomy_size\" is not specified, \"body_region\" will be used to constrain the region of generated images. It needs to be chosen from \"head\", \"chest\", \"thorax\", \"abdomen\", \"pelvis\", \"lower\".
\"anatomy_list\": If \"controllable_anatomy_size\" is not specified, the output will contain paired image and segmentation mask for the anatomy in \"./configs/label_dict.json\".
\"autoencoder_sliding_window_infer_size\": in order to save GPU memory, we use sliding window inference when decoding latents to image when \"output_size\" is large. This is the patch size of the sliding window. Small value will reduce GPU memory but increase time cost. They need to be divisible by 16.
\"autoencoder_sliding_window_infer_overlap\": float between 0 and 1. Large value will reduce the stitching artifacts when stitching patches during sliding window inference, but increase time cost. If you do not observe seam lines in the generated image result, you can use a smaller value to save inference time.

To generate images with substantial dimensions, such as 512 × 512 × 512 or larger, using GPUs with 80GB of memory, it is advisable to configure the \"num_splits\" parameter in the auto-encoder configuration to 16. This adjustment is crucial to avoid out-of-memory issues during inference.

Recommended spacing for different output sizes:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

`\"output_size\"`	Recommended `\"spacing\"`
[256, 256, 256]	[1.5, 1.5, 1.5]
[512, 512, 128]	[0.8, 0.8, 2.5]
[512, 512, 512]	[1.0, 1.0, 1.0]

Execute inference

The following code generates a synthetic image from a random sampled noise.

python -m monai.bundle run --config_file configs/inference.json\n

Execute Finetuning

Training configuration

The training was performed with the following:

GPU: at least 60GB GPU memory for 512 x 512 x 512 volume
Actual Model Input (the size of image embedding in latent space): 128 x 128 x 128
AMP: True

Run finetuning:

This config executes finetuning for pretrained ControlNet with with a new class (i.e., Kidney Tumor). When finetuning with new class names, please update configs/train.json's weighted_loss_label and configs/label_dict.json accordingly. There are 8 dummy labels as placeholders in default configs/label_dict.json that can be used for finetuning.

python -m monai.bundle run --config_file configs/train.json\n

Override the `train` config to execute multi-GPU training:

torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/multi_gpu_train.json']\"\n

Data:

The preprocessed subset of C4KC-KiTS dataset used in this finetuning config is provided in ./dataset/C4KC-KiTS_subset.

            |-*arterial*.nii.gz     # original image\n            |-*arterial_emb*.nii.gz     # encoded image embedding\nKiTS-000* --|-mask*.nii.gz      # original labels\n            |-mask_pseudo_label*.nii.gz     # pseudo labels\n            |-mask_combined_label*.nii.gz     # combined mask of original and pseudo labels\n\n

An example combined mask of original and pseudo labels is shown below:\n $\"example_combined_mask\"$

Please note that the label of Kidney Tumor is mapped to index 129 in this preprocessed dataset. The encoded image embedding is generated by provided Autoencoder in ./models/autoencoder_epoch273.pt during preprocessing to save memeory usage for training. The pseudo labels are generated by VISTA 3D. In addition, the dimension of each volume and corresponding pseudo label is resampled to the closest multiple of 128 (e.g., 128, 256, 384, 512, ...).

The training workflow requires one JSON file to specify the image embedding and segmentation pairs. The example file is located in the ./dataset/C4KC-KiTS_subset.json.

The JSON file has the following structure:

{\n    \"training\": [\n        {\n            \"image\": \"*/*arterial_emb*.nii.gz\",  # relative path to the image embedding file\n            \"label\": \"*/mask_combined_label*.nii.gz\",  # relative path to the combined label file\n            \"dim\": [512, 512, 512],  # the dimension of image\n            \"spacing\": [1.0, 1.0, 1.0],  # the spacing of image\n            \"top_region_index\": [0, 1, 0, 0],  # the top region index of the image\n            \"bottom_region_index\": [0, 0, 0, 1],  # the bottom region index of the image\n            \"fold\": 0  # fold index for cross validation, fold 0 is used for training\n        },\n\n        ...\n    ]\n}\n

References

[1] Rombach, Robin, et al. \"High-resolution image synthesis with latent diffusion models.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf

License

Code License

This project includes code licensed under the Apache License 2.0.\nYou may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Model Weights License

The model weights included in this project are licensed under the NCLS v1 License.

Both licenses' full texts have been combined into a single LICENSE file. Please refer to this LICENSE file for more details about the terms and conditions of both licenses.

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\n $\"MAISI$ \n

\n\nMAISI is based on the following papers:\n\n[**Latent Diffusion:** Rombach, Robin, et al. \"High-resolution image synthesis with latent diffusion models.\" CVPR 2022.](https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf)\n\n[**ControlNet:** Lvmin Zhang, Anyi Rao, Maneesh Agrawala; “Adding Conditional Control to Text-to-Image Diffusion Models.” ICCV 2023.](https://openaccess.thecvf.com/content/ICCV2023/papers/Zhang_Adding_Conditional_Control_to_Text-to-Image_Diffusion_Models_ICCV_2023_paper.pdf)\n\n##### Example synthetic image\n\nAn example result from inference is shown below:\n![Example synthetic image](https://developer.download.nvidia.com/assets/Clara/Images/monai_maisi_ct_generative_example_synthetic_data.png)\n\n#### Inference configuration\n\nThe inference requires:\n- GPU: at least 58GB GPU memory for 512 x 512 x 512\n- Disk Memory: at least 21GB disk memory\n\n##### Inference parameters:\n\nThe information for the inference input, like body region and anatomy to generate, is stored in [./configs/inference.json](../configs/inference.json). Please feel free to play with it. Here are the details of the parameters.\n\n- `\"num_output_samples\"`: int, the number of output image/mask pairs it will generate.\n- `\"spacing\"`: voxel size of generated images. E.g., if set to `[1.5, 1.5, 2.0]`, it will generate images with a resolution of 1.5×1.5×2.0 mm. The spacing for x and y axes has to be between 0.5 and 3.0 mm and the spacing for the z axis has to be between 0.5 and 5.0 mm.\n- `\"output_size\"`: volume size of generated images. E.g., if set to `[512, 512, 256]`, it will generate images with size of 512×512×256. They need to be divisible by 16. If you have a small GPU memory size, you should adjust it to small numbers. Note that `\"spacing\"` and `\"output_size\"` together decide the output field of view (FOV). For eample, if set them to `[1.5, 1.5, 2.0]`mm and `[512, 512, 256]`, the FOV is 768×768×512 mm. We recommend output_size is the FOV in x and y axis are same and to be at least 256mm for head, and at least 384mm for other body regions like abdomen. The output size for the x and y axes can be selected from [256, 384, 512], while for the z axis, it can be chosen from [128, 256, 384, 512, 640, 768].\n- `\"controllable_anatomy_size\"`: a list of controllable anatomy and its size scale (0--1). E.g., if set to `[[\"liver\", 0.5],[\"hepatic tumor\", 0.3]]`, the generated image will contain liver that have a median size, with size around 50% percentile, and hepatic tumor that is relatively small, with around 30% percentile. In addition, if the size scale is set to -1, it indicates that the organ does not exist or should be removed. The output will contain paired image and segmentation mask for the controllable anatomy.\n The following organs support generation with a controllable size: ``[\"liver\", \"gallbladder\", \"stomach\", \"pancreas\", \"colon\", \"lung tumor\", \"bone lesion\", \"hepatic tumor\", \"colon cancer primaries\", \"pancreatic tumor\"]``.\n The raw output of the current mask generation model has a fixed size of $256^3$ voxels with a spacing of $1.5^3$ mm. If the \"output_size\" differs from this default, the generated masks will be resampled to the desired `\"output_size\"` and `\"spacing\"`. Note that resampling may degrade the quality of the generated masks and could trigger multiple inference attempts if the images fail to pass the [image quality check](../scripts/quality_check.py).\n- `\"body_region\"`: If \"controllable_anatomy_size\" is not specified, \"body_region\" will be used to constrain the region of generated images. It needs to be chosen from \"head\", \"chest\", \"thorax\", \"abdomen\", \"pelvis\", \"lower\".\n- `\"anatomy_list\"`: If \"controllable_anatomy_size\" is not specified, the output will contain paired image and segmentation mask for the anatomy in \"./configs/label_dict.json\".\n- `\"autoencoder_sliding_window_infer_size\"`: in order to save GPU memory, we use sliding window inference when decoding latents to image when `\"output_size\"` is large. This is the patch size of the sliding window. Small value will reduce GPU memory but increase time cost. They need to be divisible by 16.\n- `\"autoencoder_sliding_window_infer_overlap\"`: float between 0 and 1. Large value will reduce the stitching artifacts when stitching patches during sliding window inference, but increase time cost. If you do not observe seam lines in the generated image result, you can use a smaller value to save inference time.\n\nTo generate images with substantial dimensions, such as 512 × 512 × 512 or larger, using GPUs with 80GB of memory, it is advisable to configure the `\"num_splits\"` parameter in [the auto-encoder configuration](./configs/config_maisi.json#L11-L37) to 16. This adjustment is crucial to avoid out-of-memory issues during inference.\n\n##### Recommended spacing for different output sizes:\n\n|`\"output_size\"`| Recommended `\"spacing\"`|\n|:-----:|:-----:|\n[256, 256, 256] | [1.5, 1.5, 1.5] |\n[512, 512, 128] | [0.8, 0.8, 2.5] |\n[512, 512, 512] | [1.0, 1.0, 1.0] |\n\n#### Execute inference\n\nThe following code generates a synthetic image from a random sampled noise.\n\n```\npython -m monai.bundle run --config_file configs/inference.json\n```\n\n### Execute Finetuning\n\n#### Training configuration\n\nThe training was performed with the following:\n- GPU: at least 60GB GPU memory for 512 x 512 x 512 volume\n- Actual Model Input (the size of image embedding in latent space): 128 x 128 x 128\n- AMP: True\n\n#### Run finetuning:\n\nThis config executes finetuning for pretrained ControlNet with with a new class (i.e., Kidney Tumor). When finetuning with new class names, please update `configs/train.json`'s `weighted_loss_label` and `configs/label_dict.json` accordingly. There are 8 dummy labels as placeholders in default `configs/label_dict.json` that can be used for finetuning.\n\n```\npython -m monai.bundle run --config_file configs/train.json\n```\n\n#### Override the `train` config to execute multi-GPU training:\n\n```\ntorchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/multi_gpu_train.json']\"\n```\n\n#### Data:\n\nThe preprocessed subset of [C4KC-KiTS](https://www.cancerimagingarchive.net/collection/c4kc-kits/) dataset used in this finetuning config is provided in `./dataset/C4KC-KiTS_subset`.\n\n```\n |-*arterial*.nii.gz # original image\n |-*arterial_emb*.nii.gz # encoded image embedding\nKiTS-000* --|-mask*.nii.gz # original labels\n |-mask_pseudo_label*.nii.gz # pseudo labels\n |-mask_combined_label*.nii.gz # combined mask of original and pseudo labels\n\n```\n\nAn example combined mask of original and pseudo labels is shown below:\n![example_combined_mask](https://developer.download.nvidia.com/assets/Clara/Images/monai_maisi_ct_generative_example_combined_mask.png)\n\nPlease note that the label of Kidney Tumor is mapped to index `129` in this preprocessed dataset. The encoded image embedding is generated by provided `Autoencoder` in `./models/autoencoder_epoch273.pt` during preprocessing to save memeory usage for training. The pseudo labels are generated by [VISTA 3D](https://github.com/Project-MONAI/VISTA). In addition, the dimension of each volume and corresponding pseudo label is resampled to the closest multiple of 128 (e.g., 128, 256, 384, 512, ...).\n\nThe training workflow requires one JSON file to specify the image embedding and segmentation pairs. The example file is located in the `./dataset/C4KC-KiTS_subset.json`.\n\nThe JSON file has the following structure:\n\n```python\n{\n \"training\": [\n {\n \"image\": \"*/*arterial_emb*.nii.gz\", # relative path to the image embedding file\n \"label\": \"*/mask_combined_label*.nii.gz\", # relative path to the combined label file\n \"dim\": [512, 512, 512], # the dimension of image\n \"spacing\": [1.0, 1.0, 1.0], # the spacing of image\n \"top_region_index\": [0, 1, 0, 0], # the top region index of the image\n \"bottom_region_index\": [0, 0, 0, 1], # the bottom region index of the image\n \"fold\": 0 # fold index for cross validation, fold 0 is used for training\n },\n\n ...\n ]\n}\n```\n\n## References\n\n[1] Rombach, Robin, et al. \"High-resolution image synthesis with latent diffusion models.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf\n\n## License\n\n### Code License\n\nThis project includes code licensed under the Apache License 2.0.\nYou may obtain a copy of the License at\n\nhttp://www.apache.org/licenses/LICENSE-2.0\n\n### Model Weights License\n\nThe model weights included in this project are licensed under the NCLS v1 License.\n\nBoth licenses' full texts have been combined into a single `LICENSE` file. 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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1468":1469,"_1470":1471,"_1472":1473,"_1474":1475},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[578,579,580,581,582],{"_583":1120,"_83":1121,"_394":1122,"_587":313,"_588":18,"_590":1123,"_592":1124},{"_583":910,"_83":911,"_394":912,"_587":32,"_588":55,"_590":913,"_592":914},{"_583":809,"_83":810,"_394":811,"_587":32,"_588":812,"_590":813,"_592":814},{"_583":594,"_83":595,"_394":596,"_587":32,"_588":597,"_590":598,"_592":599},{"_583":584,"_83":585,"_394":586,"_587":32,"_588":589,"_590":591,"_592":593},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761],[600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616],{"_617":723,"_433":723,"_394":395,"_590":724},{"_617":718,"_433":718,"_394":395,"_590":719},{"_617":714,"_433":714,"_394":395,"_590":715},{"_617":709,"_433":709,"_394":395,"_590":710},{"_617":704,"_433":704,"_394":395,"_590":705},{"_617":692,"_433":692,"_394":395,"_590":693},{"_617":680,"_433":680,"_394":395,"_590":681},{"_617":675,"_433":675,"_394":395,"_590":676},{"_617":664,"_433":664,"_394":395,"_590":665},{"_617":659,"_433":659,"_394":395,"_590":660},{"_617":654,"_433":654,"_394":395,"_590":655},{"_617":649,"_433":649,"_394":395,"_590":650},{"_617":644,"_433":644,"_394":395,"_590":645},{"_617":639,"_433":639,"_394":395,"_590":640},{"_617":634,"_433":634,"_394":395,"_590":635},{"_617":627,"_433":627,"_394":395,"_590":628},{"_617":618,"_433":618,"_394":395,"_590":619},"hostName","amazon-adsystem.com",[620,621],{"_83":626,"_623":618,"_624":32,"_394":625},{"_83":622,"_623":618,"_624":32,"_394":625},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[629,630],{"_83":633,"_623":627,"_624":32,"_394":632},{"_83":631,"_623":627,"_624":32,"_394":632},"IDE","This domain is owned by Doubleclick (Google). 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870],[815,816,817,818,819],{"_617":709,"_433":709,"_394":395,"_590":841},{"_617":675,"_433":675,"_394":395,"_590":836},{"_617":829,"_433":829,"_394":395,"_590":830},{"_617":825,"_433":825,"_394":395,"_590":826},{"_617":820,"_433":820,"_394":395,"_590":821},"nvcrm.my.site.com",[822],{"_83":823,"_623":820,"_624":32,"_394":824},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[827],{"_83":828,"_623":825,"_624":32,"_394":658},"edge","github.com",[831,832],{"_83":835,"_623":829,"_624":32,"_394":834},{"_83":833,"_623":829,"_624":32,"_394":834},"logged_in","This domain is owned by Github. 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The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. 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LICENSE	15.28 KB	February 28, 2025 UTC	curl wget Download