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Participation considerations from adversely impacted groups (protected classes) in model design and testing:	Age, Race (Flickr-Faces-HQ (FFHQ)) Only
Measures taken to mitigate against unwanted bias:	Used FFHQ public dataset comprised of 70,000 PNG images of different ages and races

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Model Overview

StyleGAN2 pretrained models for FFHQ (aligned & unaligned), AFHQv2, CelebA-HQ, BreCaHAD, CIFAR-10, LSUN dogs, and MetFaces (aligned & unaligned) datasets. This model is ready for non-commercial uses.

We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner.\nThis manifests itself as, e.g., detail appearing to be glued to image coordinates instead of the surfaces of depicted objects. We trace the root cause to careless signal processing that causes aliasing in the generator network.\nInterpreting all signals in the network as continuous, we derive generally applicable, small architectural changes that guarantee that unwanted information cannot leak into the hierarchical synthesis process.\nThe resulting networks match the FID of StyleGAN2 but differ dramatically in their internal representations, and they are fully equivariant to translation and rotation even at subpixel scales.\nOur results pave the way for generative models better suited for video and animation.

References

Model Architecture

You can see the details of this model on this link: https://nvlabs.github.io/stylegan3 and the related paper can be find here: https://nvlabs.github.io/stylegan3/

Training

You can train new networks using train.py.\nThis release contains an interactive model visualization tool that can be used to explore various characteristics of a trained model. To start it, run:

python visualizer.py\n

Dataset

StyleGAN2 pretrained models for these datasets:\nFFHQ (aligned & unaligned), AFHQv2, CelebA-HQ, BreCaHAD, CIFAR-10, LSUN dogs, and MetFaces (aligned & unaligned) datasets.

Performance

the result quality and training time depend heavily on the exact set of options. The most important ones (--gpus, --batch, and --gamma) must be specified explicitly, and they should be selected with care. See python train.py --help for the full list of options and Training configurations for general guidelines & recommendations, along with the expected training speed & memory usage in different scenarios.

The results of each training run are saved to a newly created directory, for example ~/training-runs/00000-stylegan3-t-afhqv2-512x512-gpus8-batch32-gamma8.2. The training loop exports network pickles (network-snapshot-.pkl) and random image grids (fakes.png) at regular intervals (controlled by --snap). For each exported pickle, it evaluates FID (controlled by --metrics) and logs the result in metric-fid50k_full.jsonl. It also records various statistics in training_stats.jsonl, as well as *.tfevents if TensorBoard is installed.

How to Use this Model

Requirements:

Linux and Windows are supported, but we recommend Linux for performance and compatibility reasons.
1–8 high-end NVIDIA GPUs with at least 12 GB of memory. We have done all testing and development using Tesla V100 and A100 GPUs.
64-bit Python 3.8 and PyTorch 1.9.0 (or later). See https://pytorch.org for PyTorch install instructions.
CUDA toolkit 11.1 or later.
GCC 7 or later (Linux) or Visual Studio (Windows) compilers. Recommended GCC version depends on CUDA version.

Input

Datasets are stored as uncompressed ZIP archives containing uncompressed PNG files and a metadata file dataset.json for labels.

Output

you can check the output of the model in the paper at this address:\nhttps://arxiv.org/abs/2106.12423

License

This work is made available under the Nvidia Source Code License.

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards here. Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n- https://arxiv.org/abs/2106.12423\n\n## Model Architecture\n\nYou can see the details of this model on this link: https://nvlabs.github.io/stylegan3 and the related paper can be find here: https://nvlabs.github.io/stylegan3/\n\n\n## Training\nYou can train new networks using train.py.\nThis release contains an interactive model visualization tool that can be used to explore various characteristics of a trained model. To start it, run:\n```\npython visualizer.py\n```\n### Dataset\n\nStyleGAN2 pretrained models for these datasets:\nFFHQ (aligned & unaligned), AFHQv2, CelebA-HQ, BreCaHAD, CIFAR-10, LSUN dogs, and MetFaces (aligned & unaligned) datasets.\n\n## Performance\nthe result quality and training time depend heavily on the exact set of options. The most important ones (--gpus, --batch, and --gamma) must be specified explicitly, and they should be selected with care. See python train.py --help for the full list of options and Training configurations for general guidelines & recommendations, along with the expected training speed & memory usage in different scenarios.\n\nThe results of each training run are saved to a newly created directory, for example ~/training-runs/00000-stylegan3-t-afhqv2-512x512-gpus8-batch32-gamma8.2. The training loop exports network pickles (network-snapshot-.pkl) and random image grids (fakes.png) at regular intervals (controlled by --snap). For each exported pickle, it evaluates FID (controlled by --metrics) and logs the result in metric-fid50k_full.jsonl. It also records various statistics in training_stats.jsonl, as well as *.tfevents if TensorBoard is installed.\n\n## How to Use this Model\n\nRequirements:\n\n- Linux and Windows are supported, but we recommend Linux for performance and compatibility reasons.\n- 1–8 high-end NVIDIA GPUs with at least 12 GB of memory. We have done all testing and development using Tesla V100 and A100 GPUs.\n- 64-bit Python 3.8 and PyTorch 1.9.0 (or later). See https://pytorch.org for PyTorch install instructions.\n- CUDA toolkit 11.1 or later.\n- GCC 7 or later (Linux) or Visual Studio (Windows) compilers. Recommended GCC version depends on CUDA version.\n\n\n### Input\n\nDatasets are stored as uncompressed ZIP archives containing uncompressed PNG files and a metadata file dataset.json for labels.\n\n### Output\nyou can check the output of the model in the paper at this address:\nhttps://arxiv.org/abs/2106.12423\n\n## License\nCopyright (C) 2021, NVIDIA Corporation & affiliates. All rights reserved.\n\nThis work is made available under the [Nvidia Source Code License](https://github.com/NVlabs/stylegan3/blob/main/LICENSE.txt).\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/research/models/stylegan2/bias). Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","StyleGAN2 pretrained models",["D",1731434410983],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/research/models/stylegan2","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/Nvidia-Centric-Research.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","Field | Response\n:----------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------\nParticipation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | Age, Race (Flickr-Faces-HQ (FFHQ)) Only\nMeasures taken to mitigate against unwanted bias: | Used FFHQ public dataset comprised of 70,000 PNG images of different ages and races","explainability","Field | Response |\n:------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|\nIntended Application(s) & Domain(s): | Content generation (such as Adobe Photoshop) for generating images of people, animals, etc. | \nModel Type: | Generative Adversarial Network (GAN) | \nIntended Users: | Intended for reproduction of results. | \nOutput: | Outputs image | \nDescribe how the model works: | Recognizes different classes of objects | \nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable | \nTechnical Limitations: | Discriminator is not equivariant. Operators in sRGB color space (after tone mapping). | \nVerified to have met prescribed NVIDIA standards: | Yes\nPerformance Metrics: | Fréchet inception distance (FID) | \nPotential Known Risks: | None Known | \nEnd User License Agreement: | https://github.com/NVlabs/stylegan3/blob/main/LICENSE.txt |","privacy","Field | Response |\n:----------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None | \nProtected classes used to create this model? | Age, Ancestry, Citizenship, Gender, National Origin, Race, Sexual Orientation, Skin Colors, Veteran Status\nWas consent obtained for any PII used? | Not Applicable \nHow often is dataset reviewed? | Annually | \nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Yes \nIf PII collected for the development of the model, was it collected directly by NVIDIA? | PII not collected by NVIDIA | \nIf PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable | \nIf PII collected for the development of this AI model, was it minimized to only what was required? | Yes | \nIs there data provenance? | Yes \nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes | \nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Yes |","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | Synthetic Data Generation\nDescribe the life-critical application (if present).| Not Applicable\nUse Case Restrictions: | Model for Non-Commerical Use Only\nDescribe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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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. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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