stylegan2-pytorch VS mediapipe

Add yourself a tool for generating an image of a human face. It is really simple. Just visit https://thispersondoesnotexist.com. This will generate such an image. You just have to download it and store it somewhere. Give yourself some options/parameters (i.e. location where to save the image). And write the tool in a way so that it can be integrated in coding workflow easily. For example, we just created testimonials for a website. This tool would be handy to generate a face for the testimonial giver. So I hope you get what I want.”

It's sad they used such a lame generator when Nvidia has had this near-perfect one for many years:

https://thispersondoesnotexist.com/

The website https://thispersondoesnotexist.com/ has been mentioned a few times on HN; is there a voice equivalent to this concept?

The question came to mind when we were evaluating an AI multilingual, voice-over service and someone asked about indemnification from copyright claims (eg: if this service used a voice that sounded too much like Scarlet Johanssen, for example). Surely there are purely AI voices out there immune from this, possibly even voices that are mashups of real and artificial voices that "sound real" but don't voice-print back to a real human because it's a computer mash-up... or is this a novel concept?

I wonder if we will ever get a realism model which can produce normal faces like https://thispersondoesnotexist.com instead of like super symmetrical faces of models

this has been in circulation since a while AI images took off, and they certainly weren't convincing before they did. You know the old "try to name one thing in this image" macro? Pretty sure that was AI generated, there was also thispersondoesnotexist.com which was always pretty good but of course it is

Code Examples: Check out the MediaPipe and LLM Integration Examples provided by Google AI Edge.

Hand gesture recognition model customization guide

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.