ControlNet VS mediapipe

It`s all possible. Have a look into Automatic1111`s Web UI, ControlNet, OpenPose and, if you don`t have a dedicated GPU with at least 8GB of VRAM, or at least 16GB of RAM to use the CPU, you can also use Stable Horde to use the webUI with a peer-to-peer connection, where you`ll only use a fraction of your resources, but you`ll be able to use local AI models with all the bells and whistles that you won`t get from "state-of-the-art" paid services.

Next up is ControlNet. Controlnet, as Illyasviel--creator of controlnet--describes it, "let's us control diffusion models!." ControlNet is a neural network structure to control diffusion models by adding extra connections. [8]. There is more to that than what I described, but the big take-away is that ControlNet takes a preprocessed image that you provide (or is generated) and uses that as a way of constraining the output the sampler's noise generates, allowing you to have a bit more control of the output. ControlNet is typically used for character or scene "artwork", which previously would have been a challenge with just prompting alone (at least with this current architecture).

For your first part of the comment, I can simply refer you to technologies like ControlNet, LoRA and prompt embedding: https://github.com/lllyasviel/ControlNet https://github.com/microsoft/LoRA

Here you can see what every controlnet preprocessor and model do, to give you an idea of ​​how to use

Ya, it takes awhile to really start getting comfortable with the wonkiness. If you are trying to do something specific, look for a LoRA, but in general I'd recommend you get controlnet so you can feed it a reference image. Another simple trick is to edit the image a bit in GIMP or a photo editor to get the color scheme you like and then feed it back to img2img at low denoising (0.1-0.2) to refine it. You can also add just garishly bad cartoon drawing or photoshop in assets and img2img will usually make something of them and blend them into your image, I find this easier than using img2img scribble.

If there are no errors, go to the "Extensions" tab, then "Install from URL". There, enter "https://github.com/lllyasviel/ControlNet" then click "Install".

This is a good place to start reading. Given the open-source nature of SD, there are setups of various difficulty available. A1111 is the "standard" people enjoy because it's easy to plug in new stuff (ControlNet, new models, etc.), but it's not inherently easy to set up and get going. There is an installer for it, but I haven't tried it.

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/