osmarks
7cb42e028f
I decided I wanted to integrate the experimental OCR thing better, so I rewrote in Go and also integrated the thumbnailer. However, Go is a bad langauge and I only used it out of spite. It turned out to have a very hard-to-fix memory leak due to some unclear interaction between libvips and both sets of bindings I tried, so I had Claude-3 transpile it to Rust then spent a while fixing the several mistakes it made and making tweaks. The new Rust version works, although I need to actually do something with the OCR data and make the index queryable concurrently. |
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.sqlx | ||
clipfront2 | ||
meme-rater | ||
misc | ||
src | ||
.gitignore | ||
Cargo.lock | ||
Cargo.toml | ||
clip_server_config.json | ||
clip_server.py | ||
demo-image.png | ||
formats.json | ||
frontend_config.json | ||
LICENSE | ||
mse_config.json | ||
mse.py | ||
ocr_config.json | ||
ocr.mjs | ||
ocr.py | ||
package-lock.json | ||
package.json | ||
README.md | ||
requirements.txt | ||
thumbnailer_config.json | ||
thumbnailer.py |
Meme Search Engine
Do you have a large folder of memes you want to search semantically? Do you have a Linux server with an Nvidia GPU? You do; this is now mandatory.
Features
They say a picture is worth a thousand words. Unfortunately, many (most?) sets of words cannot be adequately described by pictures. Regardless, here is a picture. You can use a running instance here.
- Infinite-scroll masonry UI for dense meme viewing.
- Online reindexing (a good reason to use it over clip-retrieval) - reload memes without a slow expensive rebuild step.
- Complex query support - query using text and images, including multiple terms at once, with weighting (including negative).
- Reasonably fast.
Setup
This is untested. It might work.
- Serve your meme library from a static webserver.
- I use nginx. If you're in a hurry, you can use
python -m http.server
.
- I use nginx. If you're in a hurry, you can use
- Install Python dependencies with
pip
fromrequirements.txt
(the versions probably shouldn't need to match exactly if you need to change them; I just put in what I currently have installed).You now need a patched version ofOpenCLIP supports SigLIP. I am now using that.transformers
due to SigLIP support.I have converted exactly one SigLIP model: https://huggingface.co/gollark/siglip-so400m-14-384. It's apparently the best one. If you don't like it, find out how to convert more. You need to download that repo.You can use any OpenCLIP model which OpenCLIP supports.
- Run
thumbnailer.py
(periodically, at the same time as index reloads, ideally) - Run
clip_server.py
(as a background service).- It is configured with a JSON file given to it as its first argument. An example is in
clip_server_config.json
.device
should probably becuda
orcpu
. The model will run on here.model
isthe OpenCLIP model to usethe path to the SigLIP model repository.model_name
is the name of the model for metrics purposes.max_batch_size
controls the maximum allowed batch size. Higher values generally result in somewhat better performance (the bottleneck in most cases is elsewhere right now though) at the cost of higher VRAM use.port
is the port to run the HTTP server on.
- It is configured with a JSON file given to it as its first argument. An example is in
- Run
mse.py
(also as a background service).- This needs to be exposed somewhere the frontend can reach it. Configure your reverse proxy appropriately.
- It has a JSON config file as well.
clip_server
is the full URL for the backend server.db_path
is the path for the SQLite database of images and embedding vectors.files
is where meme files will be read from. Subdirectories are indexed.port
is the port to serve HTTP on.
- Build clipfront2, host on your favourite static webserver.
npm install
,node src/build.js
.- You will need to rebuild it whenever you edit
frontend_config.json
.image_path
is the base URL of your meme webserver (with trailing slash).backend_url
is the URLmse.py
is exposed on (trailing slash probably optional).
- If you want, configure Prometheus to monitor
mse.py
andclip_server.py
.
MemeThresher
See here for information on MemeThresher, the new automatic meme acquisition/rating system (under meme-rater
). Deploying it yourself is anticipated to be somewhat tricky but should be roughly doable:
- Edit
crawler.py
with your own source and run it to collect an initial dataset. - Run
mse.py
with a config file like the provided one to index it. - Use
rater_server.py
to collect an initial dataset of pairs. - Copy to a server with a GPU and use
train.py
to train a model. You might need to adjust hyperparameters since I have no idea which ones are good. - Use
active_learning.py
on the best available checkpoint to get new pairs to rate. - Use
copy_into_queue.py
to copy the new pairs into therater_server.py
queue. - Rate the resulting pairs.
- Repeat 4 through 7 until you feel good enough about your model.
- Deploy
library_processing_server.py
and schedulememe_pipeline.py
to run periodically.
Scaling
Meme Search Engine uses an in-memory FAISS index to hold its embedding vectors, because I was lazy and it works fine (~100MB total RAM used for my 8000 memes). If you want to store significantly more than that you will have to switch to a more efficient/compact index (see here). As vector indices are held exclusively in memory, you will need to either persist them to disk or use ones which are fast to build/remove from/add to (presumably PCA/PQ indices). At some point if you increase total traffic the CLIP model may also become a bottleneck, as I also have no batching strategy. Indexing is currently GPU-bound since the new model appears somewhat slower at high batch sizes and I improved the image loading pipeline. You may also want to scale down displayed memes to cut bandwidth needs.