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Q-Future / Q-Instruct

②[CVPR 2024] Low-level visual instruction tuning, with a 200K dataset and a model zoo for fine-tuned checkpoints.

Home Page:https://q-future.github.io/Q-Instruct/

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cvpr2024image-quality-assessmentlow-level-vision
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Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models

CVPR 2024
1Nanyang Technological University, 2Shanghai Jiaotong University, 3Sensetime Research, 4I2R@A*STAR
*Equal contribution. #Corresponding author.

Build Local Demos

We have now supported to run the Q-Instruct demos on your own device!

See local demos for instructions. (Now support mplug_owl-2 only)

Quicker Start: the Scorer API

Install dependencies:

git clone https://github.com/X-PLUG/mPLUG-Owl.git
cd mPLUG-Owl/mPLUG-Owl2/ 
pip install -e .

Create a scorer:

from boost_qa.model import QInstructScorer as Scorer
scorer = Scorer(boost=False)

The scorer takes a PIL Image (or a list of PIL Images) as input:

from PIL import Image
images = [Image.open("fig/sausage.jpg"), Image.open("fig/examples_211.jpg")]
print(scorer(images).tolist())

The output should be [0.429931640625, 0.204833984375].

Quick Start

If your server is facing a poor connection to Hugging Face, we provide an alternative way to Download Weights from ModelScope. Click in to see details.

对于**大陆地区的使用者，若您的服务器连接Hugging Face存在一些困难，我们亦提供通过魔搭下载权重的方式。敬请点击参阅指南。

LLaVA-v1.5

Install LLaVA.

git clone https://github.com/haotian-liu/LLaVA.git
cd LLaVA
pip install -e .

Simple Interactive Demos.

See the codes and scripts below.

Example Code (Single Query) 
from llava.mm_utils import get_model_name_from_path
from llava.eval.run_llava import eval_model
model_path = "teowu/llava_v1.5_7b_qinstruct_preview_v0.1" 
prompt = "Rate the quality of the image. Think step by step."
image_file = "fig/sausage.jpg"
args = type('Args', (), {
    "model_path": model_path,
    "model_base": None,
    "model_name": get_model_name_from_path(model_path),
    "query": prompt,
    "conv_mode": None,
    "image_file": image_file,
    "sep": ",",
})()
eval_model(args)
Example Code (CLI Demo for Multi-turn Conversation) 
python -m llava.serve.cli \
    --model-path teowu/llava_v1.5_7b_qinstruct_preview_v0.1 \
    --image-file "fig/sausage.jpg" \

Note: The results may contain randomness as do_sample=True is enabled during conversation mode.

Quantitative Evaluations

Multi-choice question (MCQ) in Q-Bench. 
python eval_scripts/llava_v1.5/eval_qbench_mcq.py
Image/Video Quality Assessment

Image Quality Assessment:

python eval_scripts/llava_v1.5/eval_image_quality.py

Video Quality Assessment:

python eval_scripts/llava_v1.5/eval_video_quality.py

mPLUG-Owl-2

For mPLUG-Owl-2, Only Single GPU Inference is supported now. Please set environmental variable (e.g. export CUDA_VISIBLE_DEVICES=0) to make sure that the model can be loaded on only one device.

Install mPLUG-Owl-2.

git clone https://github.com/X-PLUG/mPLUG-Owl.git
cd mPLUG-Owl/mPLUG-Owl2/ 
pip install -e .

Simple Interactive Demos

Example Code (Single Query) 
from mplug_owl2.mm_utils import get_model_name_from_path
from eval_scripts.mplug_owl_2.run_mplug_owl2 import eval_model
model_path = "teowu/mplug_owl2_7b_448_qinstruct_preview_v0.2" 
prompt = "Rate the quality of the image. Think step by step."
image_file = "fig/sausage.jpg"
args = type('Args', (), {
    "model_path": model_path,
    "model_base": None,
    "model_name": get_model_name_from_path(model_path),
    "query": prompt,
    "conv_mode": None,
    "image_file": image_file,
    "sep": ",",
})()
eval_model(args)
Example Code (CLI Demo for Multi-turn Conversation) 
python -m mplug_owl2.serve.cli \
    --model-path teowu/mplug_owl2_7b_448_qinstruct_preview_v0.1 \
    --image-file "fig/sausage.jpg" \

Note: The results may contain randomness as do_sample=True is enabled during conversation mode.

Quantitative Evaluations

Multi-choice question (MCQ) in Q-Bench. 
python eval_scripts/mplug_owl_2/eval_qbench_mcq.py
Image/Video Quality Assessment

Image Quality Assessment:

python eval_scripts/mplug_owl_2/eval_image_quality.py

Video Quality Assessment:

python eval_scripts/mplug_owl_2/eval_video_quality.py

InternLM-XComposer-VL

InternLM-XComposer-VL has been integrated into Huggingface AutoModel (remote code mode). You can directly start with the code below without a separate install process.

Simple Interactive Demos

Example Code (Single Query) 
import torch
from transformers import AutoModel, AutoTokenizer

torch.set_grad_enabled(False)

# init model and tokenizer
model = AutoModel.from_pretrained('DLight1551/internlm-xcomposer-vl-7b-qinstruct-full', trust_remote_code=True).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained('DLight1551/internlm-xcomposer-vl-7b-qinstruct-full', trust_remote_code=True)
model.tokenizer = tokenizer

# Single-Turn Text-Image Dialogue
text = 'Describe and evaluate the quality of the image.'
image = 'fig/sausage.jpg'
response = model.generate(text, image)
print(response)
Example Code (Multi-Turn Conversation) 
import torch
from transformers import AutoModel, AutoTokenizer

torch.set_grad_enabled(False)

# init model and tokenizer
model = AutoModel.from_pretrained('DLight1551/internlm-xcomposer-vl-7b-qinstruct-full', trust_remote_code=True).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained('DLight1551/internlm-xcomposer-vl-7b-qinstruct-full', trust_remote_code=True)
model.tokenizer = tokenizer

# Multi-Turn Dialogue
text = 'Describe and evaluate the quality of the image.'
image = 'fig/sausage.jpg'
response, history = model.chat(text, image, history=None)
print(f'User: {text}')
print(f'Bot: {response}')

text = 'Which part of the pan is clearer, the top part of the bottom part?'
response, history = model.chat(text=text, image=None, history=history)
print(f'User: {text}')
print(f'Bot: {response}')

Quantitative Evaluations

Multi-choice question (MCQ) in Q-Bench. 
python eval_scripts/internlm_xcomposer_vl/eval_qbench_mcq.py
Image/Video Quality Assessment

Image Quality Assessment:

python eval_scripts/internlm_xcomposer_vl/eval_image_quality.py

Video Quality Assessment:

python eval_scripts/internlm_xcomposer_vl/eval_video_quality.py

Model Zoo

See Model Zoo. Both huggingface and modelscope weights are provided.

Training

License

Researchers and open-source developers are free to use the Q-Instruct dataset and the fine-tuned weights as provided for the four MLLMs. We also allow commercial use, while any commercial use should be pre-permitted by our team. Please email haoning001@e.ntu.edu.sg to gain the permission for commercial use.

Citation

If you consider this work interesting, please feel free to cite it in your work!

@misc{wu2023qinstruct,
      title={Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models}, 
      author={Haoning Wu and Zicheng Zhang and Erli Zhang and Chaofeng Chen and Liang Liao and Annan Wang and Kaixin Xu and Chunyi Li and Jingwen Hou and Guangtao Zhai and Geng Xue and Wenxiu Sun and Qiong Yan and Weisi Lin},
      year={2023},
      eprint={2311.06783},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

The results are upon the Q-Bench, whose bibtex is provided as follows:

@misc{wu2023qbench,
      title={Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision}, 
      author={Haoning Wu and Zicheng Zhang and Erli Zhang and Chaofeng Chen and Liang Liao and Annan Wang and Chunyi Li and Wenxiu Sun and Qiong Yan and Guangtao Zhai and Weisi Lin},
      year={2023},
      eprint={2309.14181},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

About

②[CVPR 2024] Low-level visual instruction tuning, with a 200K dataset and a model zoo for fine-tuned checkpoints.

https://q-future.github.io/Q-Instruct/

cvpr2024image-quality-assessmentlow-level-vision

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