LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data
Abstract: Existing techniques for image-to-image translation commonly have suffered from two critical problems: heavy reliance on per-sample domain annotation and/or inability of handling multiple attributes per image. Recent methods adopt clustering approaches to easily provide per-sample annotations in an unsupervised manner. However, they cannot account for the real-world setting; one sample may have multiple attributes. In addition, the semantics of the clusters are not easily coupled to human understanding. To overcome these, we present a LANguage-driven Image-to-image Translation model, dubbed LANIT. We leverage easy-to-obtain candidate domain annotations given in texts for a dataset and jointly optimize them during training. The target style is specified by aggregating multi-domain style vectors according to the multi-hot domain assignments. As the initial candidate domain texts might be inaccurate, we set the candidate domain texts to be learnable and jointly fine-tune them during training. Furthermore, we introduce a slack domain to cover samples that are not covered by the candidate domains. Experiments on several standard benchmarks demonstrate that LANIT achieves comparable or superior performance to existing models.
For unpaired image-to-image translation, (a) conventional methods (CycleGAN, MUNIT, FUNIT, StarGAN, SEMIT) require at least per-sample-level domain supervision, which is often hard to collect. To overcome this, (b) unsupervised learning methods (TUNIT, Style aware discriminator) learn image translation model using a dataset itself without any supervision, but it shows limited performance and lacks the semantic understanding of each cluster, limiting its applicability. Unlike them, (c) we present a novel framework for image translation that requires a dataset with possible textual domain descriptions (i.e., dataset-level annotation), which achieves comparable or even better performance than previous methods.
Our model LANIT is illustrated below:
git clone https://github.com/KU-CVLAB/LANIT
cd LANIT
conda env create -f environment.yaml
conda activate lanit
- Download : CelebA-HQ (PGGAN) / FFHQ (StyleGAN) / AnimalFaces (FUNIT) / Food / Lsun-Car / Lsun-Church / LHQ (ALIS) / MetFace / Anime
- Example directory hierarchy (CelebA-HQ, AnimalFaces, and other datasets):
Project
|--- LANIT
| |--- main.py
| |--- core
| |--- solver.py
| |--- data_loader.py
| |--- shell
|
| |--- datasets
| |--- CelebA-HQ
| |--- train
| |--- images
| |--- 000001.jpg
| |--- ...
| |--- test
| |--- images
| |--- 000001.jpg
| |--- ...
| |--- animal_faces
| |--- n02085620
| |--- n02085782
| |--- ...
| |--- ffhq, lsun-car, lsun-church, LHQ, metface, anime
| |--- images
| |--- 000001.jpg
| |--- ...
Then, call --train_img_dir='./datasets/CelebA-HQ/train' or './datasets/ffhq' etc.
Please refer to a function get_rpompt-and_att in ./core/utils.py
If you want to use other datasets, you shold set prompt and domain to utilize.
For example, in the case of animalfaces, the code is written as below:
if 'animal' in args.dataset:
init_prompt = 'a photo of the {}.'
base_template = ["a photo of the animal face."]
all_prompt = ['beagle', 'dandie dinmont terrier', 'golden retriever', 'malinois', 'appenzeller sennenhund', 'white fox', 'tabby cat', 'snow leopard', 'lion', 'bengal tiger']
if args.num_domains == 4:
prompt = ['beagle', 'golden retriever','tabby cat', 'bengal tiger']
elif args.num_domains == 7:
prompt = ['beagle', 'dandie dinmont terrier', 'golden retriever', 'white fox', 'tabby cat', 'snow leopard', 'bengal tiger']
elif args.num_domains == 10:
prompt = ['beagle', 'dandie dinmont terrier', 'golden retriever', 'malinois',\
'appenzeller sennenhund', 'white fox', 'tabby cat', 'snow leopard', 'lion', 'bengal tiger']
elif args.num_domains == 13:
prompt = ['beagle', 'dandie dinmont terrier', 'golden retriever', 'malinois',\
'appenzeller sennenhund', 'white fox', 'tabby cat', 'snow leopard', 'lion', 'bengal tiger',\
'french bulldog', 'mink', 'maned wolf']
elif args.num_domains == 16:
prompt = ['beagle', 'dandie dinmont terrier', 'golden retriever', 'malinois',\
'appenzeller sennenhund', 'white fox', 'tabby cat', 'snow leopard', 'lion', 'bengal tiger',\
'french bulldog', 'mink', 'maned wolf', 'monkey', 'toy poodle', 'angora rabbit']
- init_prompt: prompt format to be shared by class prompts defined by users.
- base_template: prompt that includes all the class prompts defined by users.
- prompt: the list that include class prompts defined by users.
The example shell script is located in "./shell"
- step1 setting
python main.py \
--name $name \
--dataset celeb \
--mode train \
--train_img_dir ../datasets/CelebAMask-HQ/celeba \
--val_img_dir ../datasets/CelebAMask-HQ/celeba \
--checkpoint_dir ./expr/checkpoint/lanit_celeb_weight/ \
--step1 \
--num_domains 10 \
--cycle \
--ds \
--multi_hot \
--use_base \
--zero_cut \
--w_hpf 0 \
--text_aug \
--dcycle \
--base_fix \
- step2 setting
python main.py \
--name $name \
--dataset celeb \
--mode train \
--train_img_dir ../datasets/CelebAMask-HQ/celeba \
--val_img_dir ../datasets/CelebAMask-HQ/celeba \
--checkpoint_dir ./expr/checkpoint/lanit_celeb_weight/ \
--step2 \
--num_domains 10 \
--cycle \
--ds \
--multi_hot \
--use_base \
--zero_cut \
--w_hpf 0 \
--text_aug \
--dcycle \
--resume_iter 99000 \
--p_lr 1e-6 \
--gt_update \
--lambda_dc_reg 0. \
Results are saved in the path: ./expr/results/args.name/latent(or reference).jpg
The example shell script is located in "./shell"
- Reference-guided inference code (CelebA-HQ):
python main.py \
--name $name \
--dataset celeb \
--mode sample \
--train_img_dir ../datasets/CelebAMask-HQ/celeba \
--val_img_dir ../datasets/CelebAMask-HQ/celeba \
--src_dir ../datasets/CelebAMask-HQ/celeba \
--ref_dir ../datasets/CelebAMask-HQ/celeba \
--checkpoint_dir [./checkpoints/args.dataset/args.name/, ex) ./checkpoints/celeb/celeb-10/] \
--step1 \
--num_domains 10 \
--cycle \
--ds \
--multi_hot \
--use_base \
--zero_cut \
--w_hpf 0 \
--text_aug \
--dcycle \
--base_fix \
--val_batch_size 8 \
--resume_iter [iteration at which the checkpoint is saved, ex) 98000] \
--infer_mode reference \
- Latent-guided inference code (CelebA-HQ):
python main.py \
--name $name \
--dataset celeb \
--mode sample \
--train_img_dir ../datasets/CelebAMask-HQ/celeba \
--val_img_dir ../datasets/CelebAMask-HQ/celeba \
--src_dir ../datasets/CelebAMask-HQ/celeba \
--ref_dir ../datasets/CelebAMask-HQ/celeba \
--checkpoint_dir [./checkpoints/args.dataset/args.name/, ex) ./checkpoints/celeb/celeb-10/] \
--step1 \
--num_domains 10 \
--cycle \
--ds \
--multi_hot \
--use_base \
--zero_cut \
--w_hpf 0 \
--text_aug \
--dcycle \
--base_fix \
--val_batch_size 8 \
--resume_iter [iteration at which the checkpoint is saved, ex) 98000] \
--infer_mode latent\
--latent_num 0 1 2\ # latent_num is the list that includes the attributes
If you find this research useful, please consider citing:
@article{park2022lanit,
title = {LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data},
author = {Park, Jihye and Kim, Sunwoo and Kim, Soohyun and Cho, seokju and Yoo, Jaejun and Uh, Youngjung and Kim, Seungryong},
journal = {arXiv preprint arXiv:2208.14889},
year = {2022},
}
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