StarSpace VS CLIP

This completely misunderstands what this is.

In this context "knowledge graph" means a representation of knowledge independent of it's serialization format (ie, RDF vs whatever).

It's usable wherever you have a key value (key:value of relationship) or a triple (key:relationship:something).

This is probably 85% of everything that is stored in a database, with the exception being paths that vary enough that you can't use the path as a key or relationship. In particular, most trees are suitable if you use collapse the path down to a single relationship.

I've done a bunch of work on graph embedding. They are very effective for use in anything that can be thought of as a recommendation system ("this person would like these books") or similarity ("this person is similar to these people").

Back when I was working on them I found Starspace wonderfully easy and effective: https://github.com/facebookresearch/StarSpace

In general, treating entity embedding as a standard NLP problem, and relying on their decades old techniques to create embeddings / reductions, is fairly easy, pareto-efficient for first deploy, and can use a lot of the existing infra and software. In essence, this approach is not so far away from the more advanced https://github.com/facebookresearch/StarSpace

pip install -U huggingface_hub umap-learn git+https://github.com/openai/CLIP.git

We will also need two more libraries: OpenAI’s CLIP GitHub repo, enabling us to generate image features with the CLIP model, and the umap-learn library, which will let us apply a dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP) to those features to visualize them in 2D:

Biggest missing feature for all these self hosted photo hosting is the lack of a real search. Being able to search for things like "beach at night" is a time saver instead of browsing through hundreds or thousands of photos. There are trained neural networks out there like https://github.com/openai/CLIP which are quite good.

In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:

(Github Repo | Most Popular Model | Paper | Project Page)

the first thing that comes to mind is CLIP: https://github.com/openai/CLIP

Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.

in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).

Command: "C:\StabilityMatrix\Packages\stable-diffusion-webui\venv\Scripts\python.exe" -m pip install https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip --prefer-binary

CLIP might work for your needs.