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T2IScoreScore( TS2): Objectively assessing text-to-image prompt faithfulness metrics

We introduced T2IScoreScore dataset! 📸✨ 🤗 HF Repo

This repository contains the code and models for our paper:

Who Evaluates the Evaluations? Objectively Scoring Text-to-Image Prompt Coherence Metrics with T2IScoreScore (TS2) Please check the project page for a quick overview.

T2IScoreScore is a meticulously curated dataset featuring image sets that smoothly transition from high to low faithfulness with respect to the given prompt. This collection includes both synthetic and natural examples, providing a comprehensive range for meta-evaluating existing Text-to-Image (T2I) metrics. Our goal is to facilitate the development of more accurate and consistent T2I metrics in the future.

⚡ There is a sample of an error graph for the initial prompt “A happy young woman eating a tasty orange donut” using natural images. There are two different nodes of error distance 1 from the head node of the graph:

Question Generation

Run the DSG Recipe for Question Generation

Execute the following command to generate questions using the Tifa model:

git clone git@github.com:j-min/DSG.git
cd DSG
python T2IScoreScore/src/question_generator/dsg_question_generator.py
       --api_key 'openai_api_key'
       --input_file 'src/question_generator/TS2_prompts.txt'
       --output_csv 'src/question_generator/T2S_DSG_Q_dependency.csv'

Run the Tifa Recipe for Question Generation

Execute the following command to generate questions using the Tifa model:

python T2IScoreScore/src/question_generator/tifa_question_generator.py
       --api_key 'openai_api_key'
       --input_file 'src/question_generator/TS2_prompts.txt'
       --output_csv 'src/question_generator/TS2_TIFA_Q.csv'

Replace the placeholders with appropriate values:

-a, --api_key: Your OpenAI API key. -i, --input_file: Path to the input file containing prompts. -o, --output_csv: Path to save the output CSV file.

Make sure to provide the correct paths and API key. Feel free to adjust the command for your specific needs.

Evaluated Metrics on This Dataset

Similarity Score

ClipScore
BlipScore
AlignScore

Usage

Command Line Arguments

-m, --model: Choose sim score model (clip, blip, align). -o, --output: Path to the output CSV file. -i, --image_folder: Base path for image files. -md, --metadata_file: Path to the meta-data CSV file.

Example:

python simscore.py --model 'clip' -image_folder 'data/T2IScoreScore/' -md 'data/metadata.csv' -o 'output/clipscore.csv'

Custom SimScorer Implementation

If you want to create your own similarity scorer, you can implement a class that extends the SimScorer abstract class. Use the template provided below:

from abc import ABC, abstractmethod

class CustomSimScorer(SimScorer):
    """
    Custom Similarity Scorer implementation.

    This class inherits from SimScorer and provides a template for implementing a custom similarity scoring method.

    """
    @abstractmethod
    def calculate_score(self, image, prompt):
        """
        Calculate a similarity score between an image and a prompt using the custom scoring method.

        #### Parameters
        - `image` (str): The image file name for scoring. Example: "1-0.jpg".
        - `prompt` (str): The prompt data for scoring.

        #### Returns
        - `float`: The calculated similarity score.
        """
        pass

Example usage of your custom SimScorer,

custom_scorer = MyCustomScorer()
question = 'What is in the image?'
image_path = 'path/to/your/image.jpg'
answer = custom_scorer.get_answer(question, image_path)
print(f'Generated answer: {answer}')

VQA Score

LLaVa
Fuyu
mPLUG
instructBlip
Blip1

Usage

1- PostProcess of raw answers

Command Line Arguments

‍‍-m, --model: Choose the VQA model (mplug, fuyu, llava, instructBlip, blip). -q, --questions_file: Path to the questions CSV file (tifa, dsg). -o, --output: Path to the output CSV file. -i, --image_folder: Base path for image files. -s, --start: Start index for image processing. -e, --end: End index for image processing. -md, --metadata_file: Path to meta-data CSV file.

Example:

python run_vqascor.py --model 'mplug' --questions_file 'data/TS2_TIFA_Q.csv' --output 'output/mplug_tifa.csv' --image_folder 'data/T2IScoreScore/' --start '0' --end ':' --metadata_file 'data/metadata.csv'

2- PostProcess of raw answers

Command Line Arguments

-s, --score_file: Output CSV file for scores (required). -q, --question_file: Question CSV file (required). -m, --question_gen_method: Question generation method (tifa, dsg) [default: dsg] (required). -r, --raw_answer_file: Input CSV file for raw answers (required).

Example:

python postprocess.py --score_file 'output/scores.csv' --question_file 'data/T2S_DSG_Q_dependency.csv' --question_gen_method 'dsg' --raw_answer_file 'output/raw_answers.csv'

Custom VQAScorer Implementation

If you want to test your own VQAScorer, you can create a class that extends the VQAScorer abstract class. Use the template provided below:

from abc import ABC, abstractmethod

class MyCustomScorer(VQAScorer):
    def __init__(self):
        '""
        Initialize your custom VQA scorer.
        """
        # Your initialization code here

    @abstractmethod
    def get_answer(self, question, image_path):
        """
        Generate an answer for a given question and image.

        Args:
            question (str): The question to be answered.
            image_path (str): Path to the image associated with the question.

        Returns:
            str: The generated answer to the question.
        """
        # Your custom scoring logic here
        pass

Example usage of your custom VQAScorer

custom_scorer = MyCustomScorer()
question = 'What is in the image?'
image_path = 'path/to/your/image.jpg'
answer = custom_scorer.get_answer(question, image_path)
print(f'Generated answer: {answer}')

LLM based Score

LLMScore

Usage

Command Line Arguments

--image_folder: Base path for image files. --metadata_file: Path to meta-data CSV file.

Example:

python llm_score.py --image_folder 'data/T2IScoreScore/' --metadata_file 'data/metadata.csv'