Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels

One Unified Model for Visual scoring.

Haoning Wu^1*+, Zicheng Zhang^2*, Weixia Zhang², Chaofeng Chen¹, Liang Liao¹, Chunyi Li²,

Yixuan Gao², Annan Wang¹, Erli Zhang¹, Wenxiu Sun³, Qiong Yan³, Xiongkuo Min², Guangtao Zhai^2#, Weisi Lin^1#

¹Nanyang Technological University, ²Shanghai Jiao Tong University, ³Sensetime Research

^*Equal contribution. ⁺Project Lead. ^#Corresponding author(s).

OneAlign (HF Model) | Model Zoo | Technical Report | Homepage | OneScorer (HF Space)

Results

Syllabus

Structure

[Important Note!] v1.1 <-> transformers==4.36.1+

This LLaVA-style repository has been built on transformers==4.31.0, which is incompatible with many new models available on Hugging Face. This requires to build a separate environment for the MLLM/LMM repository, which is somewhat troublesome for this visual scoring model, as we expect the Q-Align/OneAlign to effectively boost other disciplines (image/video generation, etc). Both the repository and the AutoModel (as follows) are updated to the newest version.

To this end, we have modified respective code for mPLUG-Owl2 to adapt it to the newest transformer version, i.e. transformers==4.36.1, so that you do not need to create a separate outdated environment while using it alongside other projects. The updated code is no longer compatible with the old-version Q-Align (v1.0.1/v1.0.0, and before), please update to the newest version via the following scripts:

git pull
pip install -e .

[Installation Free!] Quicker Start with Hugging Face AutoModel

No need to install this GitHub repo.

import requests
import torch
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("q-future/one-align", trust_remote_code=True, 
                                             torch_dtype=torch.float16, device_map="auto")

from PIL import Image
model.score([Image.open(requests.get("https://raw.githubusercontent.com/Q-Future/Q-Align/main/fig/singapore_flyer.jpg",
                                     stream=True).raw)], task_="quality", input_="image") # task_ : quality | aesthetics; # input_: image | video

This model also supports to be instantialized via latest IQA-Pytorch (quick install by pip install git+https://github.com/chaofengc/IQA-PyTorch.git):

import pyiqa
import torch

qalign = pyiqa.create_metric('qalign').cuda()
quality_score = qalign(input, task_='quality')
aesthetic_score = qalign(input, task_='aesthetic')

Installation

If you only need to infer (or evaluate):

git clone https://github.com/Q-Future/Q-Align.git
cd Q-Align
pip install -e .

For training, you need to further install additional dependencies as follows:

pip install -e ".[train]"
pip install flash_attn --no-build-isolation

Quick Start

We have fixed the multi-GPU inference problem.

Image Quality Scorer

• CLI Interface

export DEFAULT_IMG_PATH=fig/singapore_flyer.jpg
python q_align/evaluate/scorer.py --img_path $DEFAULT_IMG_PATH

• Python API

from q_align import QAlignScorer
from PIL import Image

scorer = QAlignScorer()
img_list = [Image.open("fig/singapore_flyer.jpg")] # can be multiple images
print(scorer(img_list).tolist())

Image Aesthetic Scorer

• CLI Interface

export DEFAULT_IMG_PATH=fig/singapore_flyer.jpg
python q_align/evaluate/scorer.py --img_path $DEFAULT_IMG_PATH --aesthetic --model-path q-future/one-align

• Python API

from q_align import QAlignAestheticScorer
from PIL import Image

scorer = QAlignAestheticScorer()
img_list = [Image.open("fig/singapore_flyer.jpg"), Image.open("fig/boy_colorful.png")] # can be multiple images
print(scorer(img_list).tolist())

Video Quality Scorer

• CLI Interface

export DEFAULT_IMG_PATH=fig/baby.mp4
python q_align/evaluate/scorer.py --img_path $DEFAULT_IMG_PATH --video --model-path q-future/one-align

• Python API

from q_align import QAlignVideoScorer, load_video

scorer = QAlignVideoScorer()
video_list = [load_video("fig/baby.mp4")]
print(scorer(video_list).tolist())

Training & Evaluation

Get Datasets

Download all datasets needed together.

import os, glob
from huggingface_hub import snapshot_download


snapshot_download("q-future/q-align-datasets", repo_type="dataset", local_dir="./playground/data", local_dir_use_symlinks=False)

gz_files = glob.glob("playground/data/*.tar")

for gz_file in gz_files:
    print(gz_file)
    os.system("tar -xf {} -C ./playground/data/".format(gz_file))

For LSVQ, (video quality dataset, optional), you can download as follows:

import os, glob
from huggingface_hub import snapshot_download

snapshot_download("teowu/LSVQ-videos", repo_type="dataset", local_dir="./playground/data/lsvq/", local_dir_use_symlinks=False)

gz_files = glob.glob("playground/data/lsvq/*.tar.gz")

for gz_file in gz_files:
    print(gz_file)
    os.system("tar -xzf {} -C ./playground/data/lsvq/".format(gz_file))

Evaluation

After preparing the datasets, you can evaluate pre-trained OneAlign as follows:

• Image Quality Assessment (IQA)

python q_align/evaluate/iqa_eval.py --model-path q-future/one-align --device cuda:0

• Image Aesthetic Assessment (IAA)

python q_align/evaluate/iaa_eval.py --model-path q-future/one-align --device cuda:0

• Video Quality Assessment (VQA)

python q_align/evaluate/vqa_eval.py --model-path q-future/one-align --device cuda:0

See our model zoo for all available models that you can use.

To convert output logits to scores, you may follow the simplest code below:

import numpy as np
    
def wa5(logits):
    logprobs = np.array([logits["excellent"], logits["good"], logits["fair"], logits["poor"], logits["bad"]])
    probs = np.exp(logprobs) / np.sum(np.exp(logprobs))
    score = np.inner(probs, np.array([5,4,3,2,1]))
    return score

LoRA Fine-tuning on OneAlign

See LoRA Fine-tuning Instruction. It only requires 2 RTX3090 GPUs.

Full Training from Start

Image Quality Assessment

• Training Q-Align with KonIQ-10k:

sh scripts/l1_koniq.sh

• Training Q-Align with mixture of KonIQ-10k, SPAQ, and KADID-10k:

sh scripts/iqa_mix.sh

Image Aesthetic Assessment

• Training Q-Align Aesthetic Predictor with AVA dataset:

sh scripts/l1_ava.sh

Video Quality Assessment

• Training Q-Align Aesthetic Predictor with AVA dataset:

sh scripts/l1_lsvq.sh

At least 4*A6000 GPUs or 2*A100 GPUs will be enough for the training.

OneAlign

• Training OneAlign with IQA datasets, AVA dataset (IAA) and LSVQ dataset (VQA):

sh scripts/onealign.sh

At least 8*A6000 GPUs or 4*A100 GPUs will be enough for the training.

Contact

Please contact any of the first authors of this paper for queries.

• Haoning Wu, haoning001@e.ntu.edu.sg, @teowu
• Zicheng Zhang, zzc1998@sjtu.edu.cn, @zzc-1998

Acknowledgements

We sincerely thank Dr Weixia Zhang (@onionbao) and Dr Chaofeng Chen (@chaofenghust) for their assistance with experiments and advice on this project.

Citation

@article{wu2023qalign,
  title={Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels},
  author={Wu, Haoning and Zhang, Zicheng and Zhang, Weixia and Chen, Chaofeng and Li, Chunyi and Liao, Liang and Wang, Annan and Zhang, Erli and Sun, Wenxiu and Yan, Qiong and Min, Xiongkuo and Zhai, Guangtai and Lin, Weisi},
  journal={arXiv preprint arXiv:2312.17090},
  year={2023},
  institution={Nanyang Technological University and Shanghai Jiao Tong University and Sensetime Research},
  note={Equal Contribution by Wu, Haoning and Zhang, Zicheng. Project Lead by Wu, Haoning. Corresponding Authors: Zhai, Guangtai and Lin, Weisi.}
}