Datasets:
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1087163166.621
num_examples: 29487
- name: test
num_bytes: 18131154
num_examples: 500
download_size: 1089858259
dataset_size: 1105294320.621
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
Dataset Card for Control-CelebA-HQ
Overview
Dataset Name: Control-CelebA-HQ
Description: An enhanced version of the CelebA-HQ dataset, Control-CelebA-HQ is specifically designed for evaluating the controlling ability of controllable generative models. This dataset is featured in the NeurIPS 2023 work titled "Controlling Text-to-Image Diffusion by Orthogonal Finetuning (OFT)", and is pivotal in evaluating the control ability of the controllable generative models.
Dataset Type: Generative Model, Controllable Generation, PEFT
Official Page: https://oft.wyliu.com/
Dataset Structure
Data Format: Images with paired facial landmarks
Size: Training set - 29.5k images; Testing set - 500 images
Resolution: High Quality (CelebA-HQ standard)
Attributes: Facial features with color-coded facial landmarks for controllable generation
Data Collection and Preparation
Source: Derived from the CelebA-HQ dataset
Collection Method: Original CelebA-HQ images processed with a standard face alignment tracker (available at https://github.com/1adrianb/face-alignment) for facial landmark detection
Data Split: 29.5k images for training, 500 images for testing
Dataset Use and Access
Recommended Uses: Training and testing controllable generative models, particularly in the context of facial image generation with landmark-based control
User Guidelines: To use the dataset, train models on the training set using facial landmarks as control signals. For testing, generate images with landmarks as control and evaluate control consistency error between input and generated image's landmarks. Please cite the OFT paper when using this dataset and protocol.
Note: Example usage and evaluation script will come out soon in Huggingface PEFT and Diffusers example. Stay tuned:D
Citation:
@InProceedings{Qiu2023OFT,
title={Controlling Text-to-Image Diffusion by Orthogonal Finetuning},
author={Qiu, Zeju and Liu, Weiyang and Feng, Haiwen and Xue, Yuxuan and Feng, Yao and Liu, Zhen and Zhang, Dan and Weller, Adrian and Schölkopf, Bernhard},
booktitle={NeurIPS},
year={2023}
}