grape-image-search 0.2.0

Find images matching keywords using CLIP. Like grep, but for images.

grape

Find images matching keywords using CLIP (Radford et al., 2021). Like grep, but for images.

$ grape -R -s --keywords sunset ~/Pictures
0.327  ~/Pictures/vacation/beach_golden_hour.jpg
0.285  ~/Pictures/vacation/pier_evening.jpg
0.241  ~/Pictures/hiking/mountain_view.jpg

Install

pip install grape-image-search

Python 3.10+. Model weights (~900 MB for the default) download on first run.

For a headless install without the --view GUI deps:

pip install grape-image-search --no-deps
pip install open-clip-torch torch Pillow platformdirs tqdm transformers sentencepiece

Usage

Paths can be files, directories (with -R), or - to read from stdin. At least one of -k or --like is required.

# text search
grape -R --keywords sunset ~/Pictures

# find images similar to a reference
grape -R --like ref.jpg ~/Pictures

# combine text + reference, and penalize something
grape -R --keywords dog --exclude cat --like my_dog.jpg ~/Pictures

# top 5 above a threshold, with scores
grape -R -s -n 5 -t 0.25 --keywords sunset ~/Pictures

# browse results in a GUI window
grape -R --keywords sunset --view ~/Pictures

# aesthetic ranking (skip default templating -- prompts are already full sentences)
grape -R -n 20 --ensemble-prompts '{}' --keywords 'beautiful photo' --exclude 'ugly photo' ~/Pictures

# read paths from stdin
find ~/Pictures -mtime -7 -type f | grape --keywords selfie -

# copy the top 10 cat photos to a folder
grape -R -print0 -n 10 --keywords cat ~/Pictures | xargs -0 cp -t ~/cats/

# open the best match directly
grape -R --keywords 'golden gate bridge' -n 1 ~/Pictures | xargs open

# interactive selection with fzf
grape -R --keywords dog ~/Pictures | fzf --preview 'chafa {}'

Run grape --help for the full flag list.

Worked examples

Per-keyword breakdown (-v) shows how each keyword and --like reference contributes. The score on the header line is the mean.

$ grape -R -v -n 3 --keywords 'playful puppy,golden retriever' ~/Pictures
0.342  /home/me/Pictures/family/biscuit_park.jpg
  playful puppy: 0.301  golden retriever: 0.383
0.298  /home/me/Pictures/family/biscuit_couch.jpg
  playful puppy: 0.267  golden retriever: 0.329
0.274  /home/me/Pictures/family/old_biscuit.jpg
  playful puppy: 0.198  golden retriever: 0.350

Calibrate, then threshold. Run with -s first to see what scores real matches land at, then set -t just below the noise floor.

$ grape -R -s --keywords sunset ~/Pictures | head -5
0.312  /home/me/Pictures/travel/santorini_evening.jpg
0.287  /home/me/Pictures/deck_sunset.jpg
0.244  /home/me/Pictures/travel/ocean_dusk.jpg
0.198  /home/me/Pictures/outdoor/golden_leaves.jpg   # weak, off-topic
$ grape -R -s -t 0.24 --keywords sunset ~/Pictures
0.312  /home/me/Pictures/travel/santorini_evening.jpg
0.287  /home/me/Pictures/deck_sunset.jpg
0.244  /home/me/Pictures/travel/ocean_dusk.jpg

Aesthetic ranking uses --exclude to subtract the mean score of a contrast prompt. Scores are smaller here because both sides are competing.

$ grape -R -s -n 5 --ensemble-prompts '{}' \
    --keywords 'beautiful photo' --exclude 'ugly photo' ~/travel/
0.163  /home/me/travel/kyoto_arashiyama.jpg
0.139  /home/me/travel/iceland_sunset.jpg
0.127  /home/me/travel/nepal_trek.jpg
0.109  /home/me/travel/paris_evening.jpg
0.091  /home/me/travel/tokyo_skyline.jpg

Scoring

Grape ranks images by cosine similarity between CLIP embeddings. Each image's score is:

mean(similarity to --keywords and --like) - mean(similarity to --exclude)

Cosine similarities are not probabilities. Meaningful matches typically sit in the 0.15-0.35 range; even strong matches rarely exceed 0.35. Use -s to calibrate before choosing a -t threshold. Scores are not comparable across models -- switch models and the numbers shift.

--like matches semantics, not pixels. Two different photos of dogs playing fetch score high; a photo and its crop do too. For actual duplicate detection, use a perceptual-hashing tool like czkawka.

Caching

On by default at ~/.cache/grape/embeddings.db ($XDG_CACHE_HOME/grape/embeddings.db). Cached:

• Image embeddings (the expensive part; ~80 ms per image on CPU)
• Text embeddings, keyed by prompt
• Model identity, so repeat runs skip the torch/open_clip import entirely
• Image-vs-non-image detection results, so videos and docs are skipped on rescan

A warm query scores thousands of images in under a second via a single matrix multiply. Entries are keyed by absolute path, file stat (size, mtime, inode), and model ID, so they auto-invalidate when files change.

Use --no-cache to disable or --cache PATH to relocate.

Models

The default is EVA-CLIP's EVA02-L-14 (Sun et al., 2023) pretrained as merged2b_s4b_b131k -- strong zero-shot accuracy at moderate cost. Pick any OpenCLIP checkpoint with --model model_name/pretrained_tag:

grape --model ViT-B-32/laion2b_s34b_b79k -R --keywords sunset ~/Pictures   # fast
grape --model ViT-L-14/laion2b_s32b_b82k -R --keywords sunset ~/Pictures   # strong

Embeddings are scoped per model; switching re-encodes everything.

Keywords are matched with prompt ensembling (Radford et al., 2021, Section 3.1.4 and Appendix A): each keyword expands into several templates ("a photo of a {}", "a photo of the {}", ...), each is embedded, then the embeddings are averaged and renormalized. Improves zero-shot accuracy over a single prompt. Override with --ensemble-prompts.