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https://github.com/pharmapsychotic/clip-interrogator

Image to prompt with BLIP and CLIP
https://github.com/pharmapsychotic/clip-interrogator

clip pytorch

Last synced: 2 days ago
JSON representation

Image to prompt with BLIP and CLIP

Host: GitHub
URL: https://github.com/pharmapsychotic/clip-interrogator
Owner: pharmapsychotic
License: mit
Created: 2022-08-09T00:31:16.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-05-15T13:32:52.000Z (7 months ago)
Last Synced: 2024-10-29T09:17:11.587Z (about 1 month ago)
Topics: clip, pytorch
Language: Python
Homepage:
Size: 394 KB
Stars: 2,683
Watchers: 29
Forks: 433
Open Issues: 61
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-segment-anything-extensions - Repo
awesome-ai-art-image-synthesis - CLIP interrogator - Convert uploaded images into prompts by [@pharampsychotic](https://twitter.com/pharmapsychotic) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/main/clip_interrogator.ipynb) (Prompt engineering / Tips and tricks)
awesome-stable-diffusion - pharmapsychotic/clip-interrogator - Jupyter notebook uses CLIP models to suggest a prompt for images similar to a given image ([Demo](https://replicate.com/methexis-inc/img2prompt)). (Training / Prompt Building)

README

        # clip-interrogator

*Want to figure out what a good prompt might be to create new images like an existing one? The **CLIP Interrogator** is here to get you answers!*

## Run it!

🆕 Now available as a [Stable Diffusion Web UI Extension](https://github.com/pharmapsychotic/clip-interrogator-ext)! 🆕




Run Version 2 on Colab, HuggingFace, and Replicate!

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/main/clip_interrogator.ipynb) [![Generic badge](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue.svg)](https://huggingface.co/spaces/pharma/CLIP-Interrogator) [![Replicate](https://replicate.com/pharmapsychotic/clip-interrogator/badge)](https://replicate.com/pharmapsychotic/clip-interrogator) [![Lambda](https://img.shields.io/badge/%CE%BB-Lambda-blue)](https://cloud.lambdalabs.com/demos/ml/CLIP-Interrogator)




Version 1 still available in Colab for comparing different CLIP models 

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/v1/clip_interrogator.ipynb) 

## About

The **CLIP Interrogator** is a prompt engineering tool that combines OpenAI's [CLIP](https://openai.com/blog/clip/) and Salesforce's [BLIP](https://blog.salesforceairesearch.com/blip-bootstrapping-language-image-pretraining/) to optimize text prompts to match a given image. Use the resulting prompts with text-to-image models like [Stable Diffusion](https://github.com/CompVis/stable-diffusion) on [DreamStudio](https://beta.dreamstudio.ai/) to create cool art!

## Using as a library

Create and activate a Python virtual environment

```bash

python3 -m venv ci_env

(for linux  ) source ci_env/bin/activate

(for windows) .\ci_env\Scripts\activate

```

Install with PIP

```

# install torch with GPU support for example:

pip3 install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu117

# install clip-interrogator

pip install clip-interrogator==0.5.4

# or for very latest WIP with BLIP2 support

#pip install clip-interrogator==0.6.0

```

You can then use it in your script

```python

from PIL import Image

from clip_interrogator import Config, Interrogator

image = Image.open(image_path).convert('RGB')

ci = Interrogator(Config(clip_model_name="ViT-L-14/openai"))

print(ci.interrogate(image))

```

CLIP Interrogator uses OpenCLIP which supports many different pretrained CLIP models. For the best prompts for 

Stable Diffusion 1.X use `ViT-L-14/openai` for clip_model_name. For Stable Diffusion 2.0 use `ViT-H-14/laion2b_s32b_b79k`

## Configuration

The `Config` object lets you configure CLIP Interrogator's processing. 

* `clip_model_name`: which of the OpenCLIP pretrained CLIP models to use

* `cache_path`: path where to save precomputed text embeddings

* `download_cache`: when True will download the precomputed embeddings from huggingface

* `chunk_size`: batch size for CLIP, use smaller for lower VRAM

* `quiet`: when True no progress bars or text output will be displayed

On systems with low VRAM you can call `config.apply_low_vram_defaults()` to reduce the amount of VRAM needed (at the cost of some speed and quality). The default settings use about 6.3GB of VRAM and the low VRAM settings use about 2.7GB.

See the [run_cli.py](https://github.com/pharmapsychotic/clip-interrogator/blob/main/run_cli.py) and [run_gradio.py](https://github.com/pharmapsychotic/clip-interrogator/blob/main/run_gradio.py) for more examples on using Config and Interrogator classes.

## Ranking against your own list of terms (requires version 0.6.0)

```python

from clip_interrogator import Config, Interrogator, LabelTable, load_list

from PIL import Image

ci = Interrogator(Config(blip_model_type=None))

image = Image.open(image_path).convert('RGB')

table = LabelTable(load_list('terms.txt'), 'terms', ci)

best_match = table.rank(ci.image_to_features(image), top_count=1)[0]

print(best_match)

```