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https://github.com/budui/Control-Units-in-StyleGAN2
The official PyTorch implementation for MM'21 paper 'Attribute-specific Control Units in StyleGAN for Fine-grained Image Manipulation'
https://github.com/budui/Control-Units-in-StyleGAN2
Last synced: about 2 months ago
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The official PyTorch implementation for MM'21 paper 'Attribute-specific Control Units in StyleGAN for Fine-grained Image Manipulation'
- Host: GitHub
- URL: https://github.com/budui/Control-Units-in-StyleGAN2
- Owner: budui
- Created: 2021-10-10T03:21:05.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2021-12-16T03:03:18.000Z (almost 3 years ago)
- Last Synced: 2024-06-08T13:34:07.381Z (4 months ago)
- Language: Python
- Homepage: https://budui.github.io/Control-Units-in-StyleGAN2/
- Size: 2.87 MB
- Stars: 34
- Watchers: 3
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# Control-Units-in-StyleGAN2
[Project](https://wrong.wang/x/Control-Units-in-StyleGAN2/) | [Paper](https://dl.acm.org/doi/10.1145/3474085.3475274)
The official PyTorch implementation for MM'21 paper 'Attribute-specific Control Units in StyleGAN for Fine-grained Image Manipulation'
## Pretrained Models
We provide the pretrained StyleGAN2 generator, face parser, attribute classifier and e4e encoder in the following link.
[Google Drive](https://drive.google.com/drive/folders/1g-ukOZ_KZXHSroLXq87jTx7iTlIinhzf?usp=sharing)
Please download the pre-trained StyleGAN2 generator at least. Put the models in `./pretrained`.
Now, the structure folder `pretrained` maybe looks like:
```text
$ tree ./pretrained
./pretrained/
|-- Attribute_CelebAMask-HQ_40_classifier.pth
|-- BiSetNet.pth
|-- correction.pt
|-- e4e_ffhq_encode.pt
|-- modifications
| |-- before_Bushy_Eyebrows_s_123,315,325.mdfc
| |-- before_alter_Black_Hair_12.mdfc
| |-- before_alter_Blond_Hair_12.mdfc
| `-- before_single_channel_11_286.mdfc
`-- stylegan2-ffhq-config-f.pt
```
## Set up the environment
Detailed setup information can be found in [environment](documents/environment.md).
We also provide the [environment.yml](environment.yml) as a reference. In general, make sure the gcc version is new enough, and all other packages can be installed via conda or pip.
## Test
Let's get started quickly:
```bash
python3 manipulation.py test ./pretrained/modifications/before_alter_Black_Hair_12.mdfc --max_factor 20
```
This command will save the editing results in `./tmp`:
```text
$ tree ./tmp
tmp
|-- before_alter_Black_Hair_12_mdfc0_batch0.jpg
`-- before_alter_Black_Hair_12_mdfc0_batch0_diff.jpg
```
The image named as `{mdfc_file_name}_{mdfc_id}_{batch_id}.jpg` is the generated manipulated grid image, and the `{mdfc_file_name}_{mdfc_id}_{batch_id}_diff.jpg` is the manipulated error image.
`{mdfc_file_name}_{mdfc_id}_{batch_id}.jpg`:
`{mdfc_file_name}_{mdfc_id}_{batch_id}_diff.jpg`:
Then, you can test other `*.mdfc` in `./pretrained/modifications`.
## Create the modification for attributes
For simplicity, let us create a new modification using a small number of positive and negative samples.
Take the attribute `Blond_Hair` as an example. We will try to find the control units for this attribute, and the modification that can change the hair color.
### open a python shell, run the following command to create a latent bank.
```python
import torch
torch.manual_seed(0)
torch.save(torch.randn(8, 512), "seed.z")
```
### generate the images corresponding to the latent.
```
python3 manipulation.py generate --num_samples 8 --batch_size 8 --separately 0 --latent_path ./seed.z --captions
```
This command will save the image `b0.jpg` in the `./tmp`.
### determine which ones to use as positive or negative samples.
The generated images are saved as `./tmp/b0.jpg`, check it and determine which image can be used as positive sample.
we select `0,1,3` as the positive samples, and `4,2,6,7` for the negative.
### generate the manipulated result only with the style vector movement.
we now calculate the mean of the positive latents and negative latents (in the S space). Then move the modulation style along the direction:
```python
python3 manipulation.py test --batch_size 8 --seed 1 --num_samples 16 --resize 256 --save_type grid \
--start_factor -3 --max_factor 6 --num_factors 5 \
--build_mdfc_way paired_delta \
--latent1 ./seed.z --ids1 0,1,3 \
--latent2 ./seed.z --ids2 4,2,6,7 \
--layers 12 --rules "(ric[10]>0.2)"
```
`latent1` & `ids1` specify the positive latent, and `latent2` & `ids2` for negative latent.
`layers` means the layer index, and `--rules "(ric[10]>0.2)"` means we select the channels that the correction to the region `hair` is greater than 0.2 .
```python
# region id mapping
# ("", ) # region_id
SEMANTIC_REGION = dict(
[
("background", (0,)), # 0
("brow", (1, 2)), # 1
("eye", (3, 4)), # 2
("glass", (5,)), # 3
("ear", (6, 7, 8)), # 4
("nose", (9,)), # 5
("mouth", (10,)), # 6
("lips", (11, 12)), # 7
("neck", (13, 14)), # 8
("cloth", (15,)), # 9
("hair", (16,)), # 10
("hat", (17,)), # 11
("face_up", (18,)), # 12
("face_middle", (19,)), # 13
("face_down", (20,)), # 14
]
)
```
Here is the result:
### save the modificaton as the mdfc file
```python
# just copy the cli arguments for the about command
PYTHONPATH=. python3 editing/modification.py paired_delta \
--latent1 ./seed.z --ids1 0,1,3 \
--latent2 ./seed.z --ids2 4,2,6,7 \
--layers 12 --rules "(ric[10]>0.2)"
```
Then change the ugly default name:
```bash
mv "tmp/alter_paired_delta_seed_(0, 1, 3)_seed_(4, 2, 6, 7).mdfc" tmp/Blond_Hair.mdfc
```
### Optimizate to find the style vector for manipulating feature maps
```bash
PYTHONPATH=. python3 ./tools/manipulation/enhance.py prev tmp/Blond_Hair.mdfc 10 \
--checkpoint pretrained/stylegan2-ffhq-config-f.pt \
--batch_size 4 --num_batch 1000
# test new mdfc
python3 manipulation.py test ./tmp/before_Blond_Hair.mdfc --batch_size 8 --seed 1 --num_samples 16 --resize 256 --save_type grid --start_factor -3 --max_factor 3 --num_factors 5 --another_factor 0.8
```
The result of full modification to control units:
# Citation
If you use this code for your research, please cite our paper [Attribute-specific Control Units in StyleGAN for Fine-grained Image Manipulation
](https://dl.acm.org/doi/10.1145/3474085.3475274)
```text
@inproceedings{10.1145/3474085.3475274,
author = {Wang, Rui and Chen, Jian and Yu, Gang and Sun, Li and Yu, Changqian and Gao, Changxin and Sang, Nong},
title = {Attribute-Specific Control Units in StyleGAN for Fine-Grained Image Manipulation},
year = {2021},
isbn = {9781450386517},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3474085.3475274},
doi = {10.1145/3474085.3475274},
booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
pages = {926–934},
numpages = {9},
keywords = {generative adversarial networks(GANs), control unit, image manipulation},
location = {Virtual Event, China},
series = {MM '21}
}
```