apiprompting 0.1.0rc1

Package for an easy implementation of paper "Attention Prompting on Image for Large Vision-Language Models".

apiprompting: Attention Prompting on Image for Large Vision-Language Models

👋 hello

Package for an easy implementation of Attention Prompting on Image for Large Vision-Language Models.

💻 install

pip install apiprompting

📄 Quick Start

clip_api

Generates image masks and blends them using CLIP_Based API.

Parameters

• images (list): list of images. Each item can be a path to image (str) or a PIL.Image.

• queries (list): list of queries. Each item is a str.

• batch_size (int): Batch size for processing images. Default is 8.

• model_name (str):
  Name of the model to load the pretrained model. Available options include "ViT-L-14-336", "ViT-L-14", and "ViT-B-32".

• layer_index (int, optional, default=22):
  Index of the layer in the model to hook. This is where the feature extraction occurs.

• enhance_coe (int, optional, default=10):
  Enhancement coefficient for mask blending, which determines the strength of the enhancement applied to the generated masks.

• kernel_size (int, optional, default=3):
  Kernel size for mask blending, which should be an odd number. This determines the size of the convolution kernel used in blending.

• interpolate_method_name (str, optional, default="LANCZOS"):
  Name of the interpolation method used for image resizing. It can be any interpolation method supported by PIL.Image.resize, such as "NEAREST", "BILINEAR", "BICUBIC", "LANCZOS", etc.

• grayscale (float, optional, default=0):
  A flag indicating whether to convert the image to grayscale. A value of 0 means no grayscale conversion, while a value of 1 will convert the image to grayscale.

Returns

• list:
  A list containing the masked images generated by the function. Each item is a PIL.Image.

llava_api

Generates image masks and blends them using the LLaVA_Based API.

Parameters

• images (list): list of images. Each item can be a path to image (str) or a PIL.Image.

• queries (list): list of queries. Each item is a str.

• batch_size (int): Batch size for processing images. Only support 1.

• model_name (str):
  Name of the model to load the pretrained model. One of "llava-v1.5-7b" and "llava-v1.5-13b".

• layer_index (int, optional, default=20):
  Index of the layer in the model to hook. This is where the feature extraction occurs.

• enhance_coe (int, optional, default=10):
  Enhancement coefficient for mask blending, which determines the strength of the enhancement applied to the generated masks.

• kernel_size (int, optional, default=3):
  Kernel size for mask blending, which should be an odd number. This determines the size of the convolution kernel used in blending.

• interpolate_method_name (str, optional, default="LANCZOS"):
  Name of the interpolation method used for image resizing. It can be any interpolation method supported by PIL.Image.resize, such as "NEAREST", "BILINEAR", "BICUBIC", "LANCZOS", etc.

• grayscale (float, optional, default=0):
  A flag indicating whether to convert the image to grayscale. A value of 0 means no grayscale conversion, while a value of 1 will convert the image to grayscale.

Returns

• list:
  A list containing the masked images generated by the function. Each item is a PIL.Image.

Example

from apiprompting import clip_api, llava_api

images, queries = ["path/to/image"], ["query"]

# CLIP_Based API
masked_images = clip_api(images, queries, model_name="ViT-L-14-336")
# LLaVA_Based API
masked_images = llava_api(images, queries, model_name="llava-v1.5-13b")

💜 acknowledgement

The README file is adopted from here.

🦸 contribution

We would love your help in making this repository even better! If you noticed any bug, or if you have any suggestions for improvement, feel free to open an issue or submit a pull request.