├── .gitignore
├── LICENSE
├── README.md
├── assets
├── cj_mix.png
├── cn2kr.png
├── compare2.png
├── compare3.png
├── cover.png
├── cursive.png
├── demo.png
├── failure.png
├── intro.gif
├── ko_wiki.gif
├── kr_demo.png
├── kr_mix.png
├── kr_mix_v2.png
├── mingchao4.png
├── network.png
├── network.svg
├── network_v2.png
├── poem.gif
├── random.png
├── reddit_bonus_humor_easter_egg.gif
├── sample_per_font.png
└── transition.png
├── charset
└── cjk.json
├── export.py
├── font2img.py
├── infer.py
├── model
├── __init__.py
├── dataset.py
├── ops.py
├── unet.py
└── utils.py
├── package.py
└── train.py
/.gitignore:
--------------------------------------------------------------------------------
1 | *.pyc
2 | datasets/
3 | experiments/
4 | .DS_Store
5 | .idea/
6 |
--------------------------------------------------------------------------------
/LICENSE:
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/README.md:
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1 | # zi2zi: Master Chinese Calligraphy with Conditional Adversarial Networks
2 |
3 |
4 |
6 |
7 | ## Introduction
8 | Learning eastern asian language typefaces with GAN. zi2zi(字到字, meaning from character to character) is an application and extension of the recent popular [pix2pix](https://github.com/phillipi/pix2pix) model to Chinese characters.
9 |
10 | Details could be found in this [**blog post**](https://kaonashi-tyc.github.io/2017/04/06/zi2zi.html).
11 |
12 | ## Network Structure
13 | ### Original Model
14 | 
15 |
16 | The network structure is based off pix2pix with the addition of category embedding and two other losses, category loss and constant loss, from [AC-GAN](https://arxiv.org/abs/1610.09585) and [DTN](https://arxiv.org/abs/1611.02200) respectively.
17 |
18 | ### Updated Model with Label Shuffling
19 |
20 | 
21 |
22 | After sufficient training, **d_loss** will drop to near zero, and the model's performance plateaued. **Label Shuffling** mitigate this problem by presenting new challenges to the model.
23 |
24 | Specifically, within a given minibatch, for the same set of source characters, we generate two sets of target characters: one with correct embedding labels, the other with the shuffled labels. The shuffled set likely will not have the corresponding target images to compute **L1\_Loss**, but can be used as a good source for all other losses, forcing the model to further generalize beyond the limited set of provided examples. Empirically, label shuffling improves the model's generalization on unseen data with better details, and decrease the required number of characters.
25 |
26 | You can enable label shuffling by setting **flip_labels=1** option in **train.py** script. It is recommended that you enable this after **d_loss** flatlines around zero, for further tuning.
27 |
28 | ## Gallery
29 | ### Compare with Ground Truth
30 |
31 |
32 |
34 |
35 | ### Brush Writing Fonts
36 |
37 |
39 |
40 | ### Cursive Script (Requested by SNS audience)
41 |
42 |
44 |
45 |
46 | ### Mingchao Style (宋体/明朝体)
47 |
48 |
50 |
51 | ### Korean
52 |
53 |
55 |
56 | ### Interpolation
57 |
58 |
60 |
61 | ### Animation
62 |
63 |
66 |
67 |
68 |
70 |
71 |
72 | ## How to Use
73 | ### Step Zero
74 | Download tons of fonts as you please
75 | ### Requirement
76 | * Python 2.7
77 | * CUDA
78 | * cudnn
79 | * Tensorflow >= 1.0.1
80 | * Pillow(PIL)
81 | * numpy >= 1.12.1
82 | * scipy >= 0.18.1
83 | * imageio
84 |
85 | ### Preprocess
86 | To avoid IO bottleneck, preprocessing is necessary to pickle your data into binary and persist in memory during training.
87 |
88 | First run the below command to get the font images:
89 |
90 | ```sh
91 | python font2img.py --src_font=src.ttf
92 | --dst_font=tgt.otf
93 | --charset=CN
94 | --sample_count=1000
95 | --sample_dir=dir
96 | --label=0
97 | --filter=1
98 | --shuffle=1
99 | ```
100 | Four default charsets are offered: CN, CN_T(traditional), JP, KR. You can also point it to a one line file, it will generate the images of the characters in it. Note, **filter** option is highly recommended, it will pre sample some characters and filter all the images that have the same hash, usually indicating that character is missing. **label** indicating index in the category embeddings that this font associated with, default to 0.
101 |
102 | After obtaining all images, run **package.py** to pickle the images and their corresponding labels into binary format:
103 |
104 | ```sh
105 | python package.py --dir=image_directories
106 | --save_dir=binary_save_directory
107 | --split_ratio=[0,1]
108 | ```
109 |
110 | After running this, you will find two objects **train.obj** and **val.obj** under the save_dir for training and validation, respectively.
111 |
112 | ### Experiment Layout
113 | ```sh
114 | experiment/
115 | └── data
116 | ├── train.obj
117 | └── val.obj
118 | ```
119 | Create a **experiment** directory under the root of the project, and a data directory within it to place the two binaries. Assuming a directory layout enforce bettet data isolation, especially if you have multiple experiments running.
120 | ### Train
121 | To start training run the following command
122 |
123 | ```sh
124 | python train.py --experiment_dir=experiment
125 | --experiment_id=0
126 | --batch_size=16
127 | --lr=0.001
128 | --epoch=40
129 | --sample_steps=50
130 | --schedule=20
131 | --L1_penalty=100
132 | --Lconst_penalty=15
133 | ```
134 | **schedule** here means in between how many epochs, the learning rate will decay by half. The train command will create **sample,logs,checkpoint** directory under **experiment_dir** if non-existed, where you can check and manage the progress of your training.
135 |
136 | ### Infer and Interpolate
137 | After training is done, run the below command to infer test data:
138 |
139 | ```sh
140 | python infer.py --model_dir=checkpoint_dir/
141 | --batch_size=16
142 | --source_obj=binary_obj_path
143 | --embedding_ids=label[s] of the font, separate by comma
144 | --save_dir=save_dir/
145 | ```
146 |
147 | Also you can do interpolation with this command:
148 |
149 | ```sh
150 | python infer.py --model_dir= checkpoint_dir/
151 | --batch_size=10
152 | --source_obj=obj_path
153 | --embedding_ids=label[s] of the font, separate by comma
154 | --save_dir=frames/
155 | --output_gif=gif_path
156 | --interpolate=1
157 | --steps=10
158 | --uroboros=1
159 | ```
160 |
161 | It will run through all the pairs of fonts specified in embedding_ids and interpolate the number of steps as specified.
162 |
163 | ### Pretrained Model
164 | Pretained model can be downloaded [here](https://drive.google.com/open?id=0Bz6mX0EGe2ZuNEFSNWpTQkxPM2c) which is trained with 27 fonts, only generator is saved to reduce the model size. You can use encoder in the this pretrained model to accelerate the training process.
165 | ## Acknowledgements
166 | Code derived and rehashed from:
167 |
168 | * [pix2pix-tensorflow](https://github.com/yenchenlin/pix2pix-tensorflow) by [yenchenlin](https://github.com/yenchenlin)
169 | * [Domain Transfer Network](https://github.com/yunjey/domain-transfer-network) by [yunjey](https://github.com/yunjey)
170 | * [ac-gan](https://github.com/buriburisuri/ac-gan) by [buriburisuri](https://github.com/buriburisuri)
171 | * [dc-gan](https://github.com/carpedm20/DCGAN-tensorflow) by [carpedm20](https://github.com/carpedm20)
172 | * [origianl pix2pix torch code](https://github.com/phillipi/pix2pix) by [phillipi](https://github.com/phillipi)
173 |
174 | ## License
175 | Apache 2.0
176 |
177 |
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