This is the official repository for the paper: Better Aligning Text-to-Image Models with Human Preference. The paper demonstrates that Stable Diffusion can be improved via learning from human preferences. By learning from human preferences, the model is better aligned with user intentions and also produces images with fewer artifacts, such as weird limbs and faces.
- [07/14/2023] The paper is accepted by ICCV 2023!
- [06/16/2023] We released HPS v2, a stronger preference prediction model trained with a much larger dataset!
- [05/10/2023] We release the full training code for adapting Stable Diffusion. Check it out!
- [03/29/2023] We released a Web demo using Gradio on Hugging Face. Check it out!
The dataset is collected from the Stable Foundation Discord server. We record human choices on images generated with the same prompt but with different random seeds. The compressed dataset can be downloaded from here. Once unzipped, you should get a folder with the following structure:
dataset
---- preference_images/
-------- {instance_id}_{image_id}.jpg
---- preference_train.json
---- preference_test.json
The annotation file, preference_{train/test}.json, is organized as:
[
{
'human_preference': int,
'prompt': str,
'id': int,
'file_path': list[str],
'user_hash': str,
'contain_name': boolean,
},
...
]
The annotation file contains a list of dict for each instance in our dataset. Besides the image paths, prompt, id and human preference, we also provide the hash of user id. The prompts with names are flagged out by the contain_name field.
The pretrained human preference classifier can be downloaded from OneDrive. Before running the human preference classifier, please make sure you have set up the CLIP environment as specified in the official repo.
import torch
import clip
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-L/14", device=device)
params = torch.load("path/to/hpc.pth")['state_dict']
model.load_state_dict(params)
image1 = preprocess(Image.open("image1.png")).unsqueeze(0).to(device)
image2 = preprocess(Image.open("image2.png")).unsqueeze(0).to(device)
images = torch.cat([image1, image2], dim=0)
text = clip.tokenize(["your prompt here"]).to(device)
with torch.no_grad():
image_features = model.encode_image(images)
text_features = model.encode_text(text)
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
text_features = text_features / text_features.norm(dim=-1, keepdim=True)
hps = image_features @ text_features.TRemember to replace path/to/hpc.pth with the path of the downloaded checkpoint.
evaluate_hps.py is a batched version of this script, and you can use it to evaluate a large number of image-text pairs.
The training script of HPC is based on OpenCLIP. We thank the community for their valuable work. The script will be released soon.
The LoRA checkpoint of the adapted model can be found here. We also provide the regularization only model trained without the guidance of human preferences at here.
You will need to have diffusers and pytorch installed in your environment. Please check this blog for details. After that, you can run the following command for inference:
python generate_images.py --unet_weight /path/to/checkpoint.bin --prompts /path/to/prompt_list.json --folder /path/to/output/folderWe highlight that you need to add 'Weird image. ' to the negative prompt when doing inference, for which the reason is explained in our paper. If you want to do inference on AUTOMATIC1111/stable-diffusion-webui, please check this issue.
- We also provide a UI for testing our method that is built with gradio. Running the following command in a terminal will launch the demo:
# install dependencies pip install -r gradio_requirements.txt python app_gradio.py - This demo is also hosted on HuggingFace here.
# environments
pip install -r requirements.txt # download the regularization images to regularization_images
python download_regularization_images.py
# This script will download all the images of LAION aesthetics 6.5+. In our experiments, ~200K regularization data is enough.
# Annotating HPS for images from DiffusionDB
python process_diffusiondb.py --clip_checkpoint /path/to/downloaded/checkpoint.pth --meta_file diffusiondb_hps.jsonl
# Select images for training
python select_training_images.py --positive_folder positive --negative_folder negative --meta_file diffusiondb_hps.jsonl --output_meta annotation.jsonl After generating the annotation, you may want to adjust the number of positive and negative samples used for SD training. In our experiments, we use 37K positive images and 21K negative images. You may need to tune the ratio to better fit your need. Before training, you also need to specify $work_dir in your environment, or specify it directly in the script. Don't forget to create a file named validation_prompts.txt, with each line as a prompt, so the training can be tracked.
export work_dir=logs
echo "your prompt here" >> validation_prompts.txt
The training script is launched via accelerator, so you need to create a configuration file in advance by running accelerate config. This allows you to train on multiple machines / GPUs. You will need to change the batch size in the script if you are using more than one GPU. For example, if you have machine with 8 GPUs, you should change the batch size to 5 rather than 40.
# Train LoRA weights for Stable Diffusion!
bash scripts/train_lora.sh
# Train LoRA weights for Stable Diffusion XL!
bash scripts/train_lora_sdxl.sh
Visualization of SDXL Lora 1.0
If you find the work helpful, please cite our paper:
@misc{wu2023better,
title={Better Aligning Text-to-Image Models with Human Preference},
author={Xiaoshi Wu and Keqiang Sun and Feng Zhu and Rui Zhao and Hongsheng Li},
year={2023},
eprint={2303.14420},
archivePrefix={arXiv},
primaryClass={cs.CV}
}