metadata
license: mit
pipeline_tag: image-text-to-text
CoLLaVO model
This repository contains the weights of the model presented in CoLLaVO: Crayon Large Language and Vision mOdel.
Simple running code is based on MoAI-Github.
You need only the following seven steps.
Simple running code is based on CoLLaVO-Github.
You need only the following seven steps.
[0] Download Github Code of CoLLaVO, install the required libraries, set the necessary environment variable (README.md explains in detail! Don't Worry!).
git clone https://github.com/ByungKwanLee/CoLLaVO
bash install
[1] Loading Image
from PIL import Image
from torchvision.transforms import Resize
from torchvision.transforms.functional import pil_to_tensor
image_path = "figures/crayon_image.jpg"
image = Resize(size=(490, 490), antialias=False)(pil_to_tensor(Image.open(image_path)))
[2] Instruction Prompt
prompt = "Describe this image in detail."
[3] Loading CoLlaVO
from collavo.load_collavo import prepare_collavo
collavo_model, collavo_processor, seg_model, seg_processor = prepare_collavo(collavo_path='BK-Lee/CoLLaVO-7B', bits=4, dtype='fp16')
[4] Pre-processing for CoLLaVO
collavo_inputs = collavo_model.demo_process(image=image,
prompt=prompt,
processor=collavo_processor,
seg_model=seg_model,
seg_processor=seg_processor,
device='cuda:0')
[5] Generate
import torch
with torch.inference_mode():
generate_ids = collavo_model.generate(**collavo_inputs, do_sample=True, temperature=0.9, top_p=0.95, max_new_tokens=256, use_cache=True)
[6] Decoding
answer = collavo_processor.batch_decode(generate_ids, skip_special_tokens=True)[0].split('[U')[0]
print(answer)