clip-interrogator-with-less-VRAM

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!

This version uses less VRAM than the main repo by only having one model loaded at a time.

When you create an Interrogator:

ci = Interrogator(Config())

The BLIP and CLIP models are both loaded, but only BLIP is on the GPU, the CLIP stays in RAM.

When you actually do inference:

ci.interrogate(image)
# Or:
# ci.interrogate_classic(image)
# ci.interrogate_fast(image)

BLIP inference is done, it gets unloaded then CLIP gets loaded and infers. If you run it again, CLIP is done first, then BLIP is loaded, to reduce pointless loading and unloading.

By using this, it (classic or fast, normal doesn't quite fit) can be run on as little as 4GB of VRAM, the main repo needing at least 6GB.

But wouldn't loading a new model every time I want to interrogate an image be terrible for performance?

- me

Absolutely.

There's little performance overhead for just one interrogation, since it's essentially lazy loading the CLIP model, but for multiple images, there will be a noticable effect.

That's why I made the interrogate_batch functions:

# files = Some list of strings
images = [Image.open(f).convert("RGB") for f in files]
ci.interrogate_batch(images)

This does BLIP inference on each of the images, then loads the CLIP model, saving some performance. There are also interrogate_{classic,fast}_batch functions.

Run it!

Bash (linux/unix):

$ ./run_cli.py -i input.png -m $MODE

Windows:

$ python run_cli.py -i input.png -m $MODE

Where $MODE is either best, classic or fast (default best)

Run Version 2 on Colab, HuggingFace, and Replicate!

Version 1 still available in Colab for comparing different CLIP models

About

The CLIP Interrogator is a prompt engineering tool that combines OpenAI's CLIP and Salesforce's BLIP to optimize text prompts to match a given image. Use the resulting prompts with text-to-image models like Stable Diffusion on DreamStudio to create cool art!

Using as a library

Create and activate a Python virtual environment

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==0.4.3

You can then use it in your script

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

See the run_cli.py and run_gradio.py for more examples on using Config and Interrogator classes.