cyclereward 0.1.5

Image-text alignment metric trained on cycle consistency preferences

Cycle Consistency as Reward: Learning Image-Text Alignment without Human Preferences

Paper | Project Page | Dataset (I2T) | Dataset (T2I) | Dataset Viewer

Hyojin Bahng*, Caroline Chan*, Fredo Durand, Phillip Isola.
(*Equal contribution, alphabetical order.)
MIT CSAIL.

CycleReward is a reward model trained on preferences derived from cycle consistency. Given a forward mapping $$F:X \rightarrow Y$$ and a backward mapping $$G: Y \rightarrow X$$, we define cycle consistency score as the similarity between the original input $$x$$ and its reconstruction $$G(F(x))$$. This score serves as a proxy for preference: higher cycle consistency indicates a preferred output. This provides a more scalable and cheaper signal for learning image-text alignment compared to human supervision. We construct CyclePrefDB, a large-scale preference dataset comprising 866K comparison pairs spanning image-to-text and text-to-image generation, with an emphasis on dense captions and prompts. Trained on this dataset, CycleReward matches or surpasses models trained on human or AI feedback.

Quick Start

Run pip install cyclereward. The following Python code is all you need.

The basic use case is to measure the alignment between an image and a caption. A higher score means more similar, lower means more different. We release three model variants: CycleReward-Combo, CycleReward-I2T, CycleReward-T2I.

from cyclereward import cyclereward
from PIL import Image
import torch 

device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = cyclereward(device=device, model_type="CycleReward-Combo")

caption = "a photo of a cat"
image = preprocess(Image.open("image_path")).unsqueeze(0).to(device)
score = model.score(image, caption) 

CyclePrefDB Dataset

CycleReward is trained on CyclePrefDB, a large-scale preference dataset based on cycle consistency. We provide comparison pairs for both image-to-text (I2T) and text-to-image (T2I) generation, with a focus on dense captions and prompts.

Dataset	Number of Pairs
CyclePrefDB-I2T	398K
CyclePrefDB-T2I	468K

You can use the Hugging Face Datasets library to load the datasets:

from datasets import load_dataset

# Load dataset
dataset = load_dataset("carolineec/CyclePrefDB-I2T", split='train')

Citation

If you find our work or any of our materials useful, please cite our paper:

@article{bahng2025cycle,
    title={Cycle Consistency as Reward: Learning Image-Text Alignment without Human Preferences},
    author= {Bahng, Hyojin and Chan, Caroline and Durand, Fredo and Isola, Phillip},
    journal={arXiv preprint arXiv:2506.02095},
    year={2025}
}