license: apache-2.0
language:
- en
pipeline_tag: text-to-image
ImageReward
🤗 HF Repo • 🐦 Twitter • 📃 Paper
ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
ImageReward is the first general-purpose text-to-image human preference RM which is trained on in total 137k pairs of expert comparisons, based on text prompts and corresponding model outputs from DiffusionDB. We demonstrate that ImageReward outperforms existing text-image scoring methods, such as CLIP, Aesthetic, and BLIP, in terms of understanding human preference in text-to-image synthesis through extensive analysis and experiments.
Quick Start
Install Dependency
We have integrated the whole repository to a single python package image-reward. Following the commands below to prepare the environment:
# Clone the ImageReward repository (containing data for testing)
git clone https://github.com/THUDM/ImageReward.git
cd ImageReward
# Install the integrated package `image-reward`
pip install image-reward
Example Use
We provide example images in the assets/images directory of this repo. The example prompt is:
a painting of an ocean with clouds and birds, day time, low depth field effect
Use the following code to get the human preference scores from ImageReward:
import os
import torch
import ImageReward as reward
if __name__ == "__main__":
prompt = "a painting of an ocean with clouds and birds, day time, low depth field effect"
img_prefix = "assets/images"
generations = [f"{pic_id}.webp" for pic_id in range(1, 5)]
img_list = [os.path.join(img_prefix, img) for img in generations]
model = reward.load("ImageReward-v1.0")
with torch.no_grad():
ranking, rewards = model.inference_rank(prompt, img_list)
# Print the result
print("\nPreference predictions:\n")
print(f"ranking = {ranking}")
print(f"rewards = {rewards}")
for index in range(len(img_list)):
score = model.score(prompt, img_list[index])
print(f"{generations[index]:>16s}: {score:.2f}")
The output should be like as follow (the exact numbers may be slightly different depending on the compute device):
Preference predictions:
ranking = [1, 2, 3, 4]
rewards = [[0.5811622738838196], [0.2745276093482971], [-1.4131819009780884], [-2.029569625854492]]
1.webp: 0.58
2.webp: 0.27
3.webp: -1.41
4.webp: -2.03