rclip VS CLIP

The installation instructions are in the project README: https://github.com/yurijmikhalevich/rclip. I use the prebuilt executable option to install it on my Synology NAS.

rclip source code is published on GitHub under the MIT license: https://github.com/yurijmikhalevich/rclip. Give it a try, and let me know what you think!

Use this command line project called rclip which is the easiest way to use CLIP models on your local folders of images. It was written by /u/39dotyt (thanks again!).

https://github.com/yurijmikhalevich/rclip https://mikhalevi.ch/rclip-an-ai-powered-command-line-photo-search-tool

The latter would be easy to use with https://github.com/yurijmikhalevich/rclip .

For images rclip can give you search that's better than Google Photos with entirely unannotated data and pure natural English queries.

Google Photos can do this kind of searching for specific objects and combinations. I'm not sure if there is a tool that you can install locally that will do it. What we're probably looking for is a local image search engine powered by CLIP. Something like this, but with a GUI on Windows. It would be cool if tools like Eagle integrated CLIP into them, so you didn't have to manually tag everything, they were just auto-tagged with the content that is already in them. PhotoPrism might be the closest thing yet.

Indexing was done with this other guy's github project. It was convenient because it automatically handles things like "continue where it left off".

It's based on https://github.com/yurijmikhalevich/rclip who posted his project here a while ago.

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.

pip install deepfloyd_if==1.0.2rc0 pip install xformers==0.0.16 pip install git+https://github.com/openai/CLIP.git --no-deps pip install huggingface_hub --upgrade