Riru VS mediapipe

https://github.com/RikkaApps/Riru it is the precursor to zygisk. It should still work since the developer is still releasing modules that support it

That's not the best solution, but try Riru version of LSPosed. It requires an additional module (https://github.com/RikkaApps/Riru/releases), but if that would work for you, it's better than nothing.

Install Riru-Core: https://github.com/RikkaApps/Riru/releases.

Riru

(Get the Riru version if you are gonna use magisk v23, but you must install riru's main module before that: https://github.com/RikkaApps/Riru/releases ) (Get the Zygisk version if you are gonna use magisk v24)

Riru: https://github.com/RikkaApps/Riru/releases/tag/v26.1.3LSPosed : https://github.com/LSPosed/LSPosed/releases/tag/v1.6.0

Download the latest version of the Magisk module with an interesting name Riru and install it via Magisk

Xposed wont be updated after Android 7.0. So I use edxposed instead;Edxposed depends on Riru-core so install Riru-core first: https://github.com/RikkaApps/Riru/releases after downlad copy it into the phone install it by Magisk then restart(重启) download EdXposed https://github.com/ElderDrivers/EdXposed/releases/ Install EdXposedin the same way as installing Riru-core

mediapipe/docs/solutions/pose.md at master · google/mediapipe · GitHub

Today, we announce MediaPipe diffusion plugins, which enable controllable text-to-image generation to be run on-device. Expanding upon our prior work on GPU inference for on-device large generative models, we introduce new low-cost solutions for controllable text-to-image generation that can be plugged into existing diffusion models and their Low-Rank Adaptation (LoRA) variants.

You can just grab the TFLite version! https://github.com/google/mediapipe/blob/master/docs/solutions/models.md

I believe Google already released transformers under an apache 2 license with a patent grant:

https://github.com/google/mediapipe/blob/master/mediapipe/mo...

I was going to say that I like the MediaPipe Selfie Segmentation model for doing this sort of thing in a web page, but I've just noticed (when getting the GitHub link[1]) that Google have marked the code as legacy[2] ... no idea if the new solution is better/easier to use[3].

For what it's worth, my CodePen using the old model is here: https://codepen.io/kaliedarik/pen/PopBxBM

[1] - https://github.com/google/mediapipe/blob/master/docs/solutio...

[2] - "Attention: Thank you for your interest in MediaPipe Solutions. As of April 4, 2023, this solution was upgraded to a new MediaPipe Solution."

[3] - https://developers.google.com/mediapipe/solutions/vision/ima...

I have used mediapipe very successfully in multiple projects and it's very easy to get running. You can choose from many different vision tasks including hand landmarks ( https://github.com/google/mediapipe/blob/master/docs/solutions/hands.md )

I found MediaPipe's Face Mesh and was impressed with how simple it was to get going, but it just gives you the landmark points and I've not gone any further yet.

We've trained ControlNet on a subset of the LAION-Face dataset using modified output from MediaPipe's face mesh annotator to provide a new level of control when generating images of faces.

Medipaipe library link: https://mediapipe.dev/