Face Swap Android Github

[Beichen Poque]

FaceSwapLabis an extension for Stable Diffusion that simplifies the use of insighface models for face-swapping. It has evolved from sd-webui-faceswap and some part of sd-webui-roop. However, a substantial amount of the code has been rewritten to improve performance and to better manage masks.

Some key features include the ability to reuse faces via checkpoints, multiple face units, batch process images, sort faces based on size or gender, and support for vladmantic. It also provides a face inpainting feature.

This extension is not intended to facilitate the creation of not safe for work (NSFW) or non-consensual deepfake content. Its purpose is to bring consistency to image creation, making it easier to repair existing images, or bring characters back to life.

We will comply with European regulations regarding this type of software. As required by law, the code may include both visible and invisible watermarks. If your local laws prohibit the use of this extension, you should not use it.

Modify correct domain XML file below to point to the downloaded path of your image. For instance, if the downloaded faceswap-server-release.qcow is at /home/junjuew/faceswap-server-release.qcow. Then the xml file should be modified into:

The cloudlet image takes a bit longer to be fully booted up and initialized. You can monitor whether the virtual machine has fully booted up by checking whether you've arrived at log-in shell through virt-manager console.

If you want to customize the content of the image, the default username:password is faceswap-admin:faceswap-admin. Password-based ssh in Cloudlet images are by default turned on. You're advised to change the password as soon as you gain access.

ONNX Runtime version 1.10 and earlier: The source files are modified directly. If you wish to go back to creating a full build, or wish to change the operator kernels included, you MUST run git reset --hard or git checkout HEAD -- ./onnxruntime/core/providers from the root directory of your local ONNX Runtime repository to undo these changes.

If the configuration file is created using ORT format models, the input/output types that individual operators require can be tracked if --enable_type_reduction is specified. This can be used to further reduce the build size if --enable_reduced_operator_type_support is specified when building ORT.

In this section, ops.config is a configuration file that specifies the opsets, op kernels, and types to include. See the configuration file used by the pre-built mobile packages at tools/ci_build/github/android/mobile_package.required_operators.config.

The build options are specified with the file provided to the --build-settings-file option. See the current build options used by the pre-built mobile package at tools/ci_build/github/apple/default_mobile_ios_framework_build_settings.json. You can use this file directly.

The reduced set of ops in the custom build is specified with the file provided to the --include_ops_by_config option. See the current op config used by the pre-built mobile package at tools/ci_build/github/android/mobile_package.required_operators.config (Android and iOS pre-built mobile packages share the same config file). You can use this file directly.

The default package does not include the training APIs. To create a training package, add --enable_training_apis in the build options file provided to --build-settings-file and add the --variant Training option when calling build_and_assemble_apple_pods.py.

Note: The onnxruntime-mobile-objc pod depends on the onnxruntime-mobile-c pod. If the released onnxruntime-mobile-objc pod is used, this dependency is automatically handled. However, if a local onnxruntime-mobile-objc pod is used, the local onnxruntime-mobile-c pod that it depends on also needs to be specified in the Podfile.

The build options are specified with the file provided to the --build_settings option. See the current build options used by the pre-built mobile package at tools/ci_build/github/android/default_mobile_aar_build_settings.json.

The reduced set of ops in the custom build is specified with the file provided to the --include_ops_by_config option. See the current op config used by the pre-built mobile package at tools/ci_build/github/android/mobile_package.required_operators.config.

The --build_settings and --include_ops_by_config options are both optional and will default to what is used to build the pre-built mobile package. Not specifying either will result in a package like the pre-built mobile package.

In addition to being an open source 2D&3D deep face analysis library, InsightFace also offers a range of commercial products. These include solutions for high quality face swapping and SDK development for custom applications. We are committed to providing advanced tools that drive innovation and creativity across various industries.

Although still in their infancy, a growing number ofrecently-filed lawsuits associated with generative artificialintelligence (AI) training practices, products, and services haveprovided a meaningful first look into how US courts may address theprivacy, consumer safety, and intellectual property protectionconcerns that have been raised by this new, and inherentlyevolving, technology. The legal theories that have served as thebasis of recent claims have varied widely, are often overlapping,and have included invasion of privacy and property rights; patent,trademark, and copyright infringement; libel and defamation; andviolations of state consumer protection laws, among others.

To date, courts have appeared reluctant to impose liability onAI developers and have expressed skepticism of plaintiffs'rhetoric around AI's purported world-ending potential. Courtshave also found a number of recent complaints to be lacking in thespecific, factual, and technical details necessary to proceedbeyond the pleadings stage. This alert aims to provide a snapshotof the current litigation landscape in the rapidly growing field ofgenerative-AI law in the United States.

Over a two-week period in June and July, 2023, a number offederal class action lawsuits were filed in the US District Courtfor the Northern District of California, many by the same law firm,against the developers of some of the most well-known generative AIproducts on the market, including OpenAI, Inc. (OpenAI) andAlphabet Inc./Google LLC (Google).

On 11 July 2023, in J.L. v. Alphabet Inc.,3the same plaintiffs' firm as in P.M. filed a similarclass action complaint against Google, asserting both privacy andcopyright law violations. As with P.M., the plaintiffsraise theoretical concerns relating to the proliferation ofAI.4 The plaintiffs in J.L. argued that many ofGoogle's generative AI products, including Bard (a textgenerator), Imagen and Gemini (two text-to-image diffusion models),MusicLM (a text-to-music tool), and Duet AI (a data visualizationtool), all relied on training data that Google collected from theInternet. The complaint does note that Google was transparent anddisclosed its data gathering practices, but suggested that Googleshould have explored other options for the development of trainingdata, such as purchasing from the commercial data market.Additionally, and without any specific evidence, the plaintiffsargued that Google violated the Copyright Act because: (1)Google's AI products allegedly used copyrighted text, music,images, and data for training purposes; and (2) the AI productsthemselves, as well as their expressive output, constituteinfringing derivative works. Like the plaintiffs in P.M.,the plaintiffs in J.L. are seeking broad injunctive reliefaimed at restricting Google's generative AI products. Theseplaintiffs are also seeking further specific relief with respect totheir claims for copyright infringement, as well as various formsof class-wide damages related to its theories.

One of the most well-known cases alleging copyright infringementis Andersen v. Stability AI Ltd.5 In that case,plaintiffs Sarah Andersen, Kelly McKernan, and Karla Ortiz, onbehalf of a putative class of artists, alleged that Stability AI,Ltd. and Stability AI, Inc. (collectively, Stability AI) and othersscraped billions of copyrighted images from online sources, withoutpermission, in order to train their image-generating models toproduce seemingly new images without attribution to the originalartists who supplied the training material. They further arguedthat this practice deprived artists of commissions and allowed thedefendants to profit from the artists' copyrighted works. Intheir motion to dismiss, the defendants argued that the models donot copy or store any images, copyrighted or otherwise. Rather, thedefendants explained, their models only analyze the properties ofonline images to generate parameters that were later used to assistthe model in creating new and unique images from text prompts, asopposed to reproducing or copying any portion of the underlyingimages used for training.

At a hearing on the defendants' motion to dismiss on 19 July2023, Judge William Orrick expressed skepticism regarding theplaintiffs' claims indicating he would tentatively dismissthem. Specifically, he explained that: (1) the images produced bythe models are not "substantially similar" toplaintiffs' art; and (2) because the models had been trained on"five billion compressed images" it is "implausiblethat [plaintiffs'] works are involved" in the creation ofthose images. Judge Orrick did, however, provide plaintiffs with anopportunity to amend their complaint "to provide morefacts" proving otherwise.6

GitHub, Inc. (GitHub), the well-known online code repository, isalso the subject of a putative class action filed in November 2022in the Northern District of California under the caption Doe v.GitHub, Inc.7 In that case, the anonymousplaintiffs are developers who allegedly published licensed code onGitHub's website and claim that GitHub used that code to trainits AI-powered coding assistant, Copilot. The developer-plaintiffssued GitHub, Microsoft, and OpenAI alleging violations of privacyand property rights, including violation of copyright managementlaws based on GitHub's purported use of licensed materialswithout appropriate attribution.

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