StabilityMatrix VS CLIP

On Windows, StabilityMatrix (https://github.com/LykosAI/StabilityMatrix) is a very easy way to get any (or all) of those wrappers installed without conflicts.

do you have a good pc/laptop with a good GPU? is so start with this. A1111 WebUI is no longer being updated so heres a new one https://github.com/LykosAI/StabilityMatrix/ this site you can download checkpoints and loras you have to sign up (its free, and once you do that click on the eye and click everything) https://civitai.com/ you can get prompts from this site (use the search) https://playgroundai.com/ Edited: took out the direct link out and reposted

I then thought, well I just do a new fresh install of A1111. I did 3 different fresh installs and did an install with https://github.com/LykosAI/StabilityMatrix as a portable installation to try and eliminate any outside interference. No luck all installs make pictures horrible, I mean bad, even the portable installation.

If you have a computer powerful enough to run SD, you can install one of the "software" from Stable Diffusion > Local install, the most popular ones are A111, Vlad and comfyUI (but I would advise to start with the first two, as comfyUI may be too complex at the begining). You can install them via StabilityMatrix ; it's a software that manage the installation of the popular webui, their requirements, and their versions; if you're afraid to manage the installation of pyton/miniconda/other things required to run it. NMKD is also a good alternative, as it should work right out of the box (it can even download the base model, if I remember correctly). If you haven't a computer good enough, look at Stable diffusion > Online Websites.

I'd install either via https://github.com/LykosAI/StabilityMatrix or https://civitai.com/models/27574/super-easy-ai-installer-tool.

You can view our source code on our GitHub

Update v1.1 of Stability Matrix has arrived!

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.