clip-interrogator VS semantic-kernel

It's image -> words, the inverse of stable diffusion.

see: https://github.com/pharmapsychotic/clip-interrogator

I have been using this on a CPU https://github.com/pharmapsychotic/clip-interrogator, I tried a lot of pre-trained models combinations, all are slow.

I'm addition to controlnet, try the clip interrogator to see how clip would describe the image and then use that language in your prompt. You can try the whole image or cropped portions. There is a colab available if you don't want to run it locally.

In my current process, I use CLIP Interrogator to produce a high level caption and wd14 tagger for more granular booru tags. Typically in that order, because you can append the results from the latter to the former. Both tools perform with greater accuracy than the standard interrogators in img2img and give you more flexibility and features as well. You still have to do some manual adjustments, but I generally prefer this process over starting from scratch.

If you are interested in this, definitely see if you can get some of the OSS models running and get a feel for how to interrogate them. Maybe see if you can get some mileage out of the CLIP-Interrogator

Next, I used the lists of artists, flavors, mediums, movements, and negatives that are used for the clip-interrogator and pasted these in the chat and told the bot to categorize them accordingly. As you can only paste up to certain characters in single message (4-5K in 3.5 and 6-8K in 4).

Here’s the specific one I’m using (runs in browser)

I really enjoy using the CLIP Interrogator on huggingspaces, but it is often super slow and sometimes straight up breaks. Now it is possible to locally install it, https://github.com/pharmapsychotic/clip-interrogator but I don't know if its viable to run on a laptop with 6gb videocard anyway.

There is an amazing sample on how to create your own LLM Service class to be used in Semantic Kernel. You can view the Sample here: https://github.com/microsoft/semantic-kernel/blob/3451a4ebbc9db0d049f48804c12791c681a326cb/dotnet/samples/KernelSyntaxExamples/Example16_CustomLLM.cs

This week, I had the chance to explore the SemanticKernel code base, particularly the core plugins. SemanticKernel comes equipped with these built-in plugins:

In this article we'll look at the high-level capabilities building AI orchestration systems in C# with Semantic Kernel, a rapidly maturing open-source AI orchestration framework.

I'm using Semantic Kernel (https://github.com/microsoft/semantic-kernel) and it's really nice. Makes building more complex workflows really simple without sacrificing control.

A bunch of examples (https://github.com/microsoft/semantic-kernel/blob/main/dotne...) for how to handle just about anything you need to do with OAI with a lot less boilerplate.

I haven't tried it but there's Microsoft semantic-kernel.

https://github.com/microsoft/semantic-kernel

Semantic Kernel github.com/microsoft/semantic-kernel

The need for efficient model routing has been a point of discussion in the community. Addressing this, I've submitted a pull request to Semantic-Kernel that introduces an automated multi-model connector.