ACGAN Conditional Anime Generation

Note: I have restructured the whole folder, cleaned up training code, pruned my dataset, and updated most of the results. You can see the old version of this repo in old.

Generate colorful anime characters using GAN.

By interpolating generator input, we can see some interesting results.

By fixing color classes and varying noise, we can generate anime characters with same colors but different identity.

Start traning

Modify config.yaml as you wish.

> python3

The dataset

The current dataset is a composition of 2 datasets:

  1. One dataset with eye and hair color labels (30k+ images)
  2. One dataset with year label, which is the year in which the anime is produced (60k+ images).

The dataset format is as follows:

- images/
    - XXXXX.jpg
    - ...
- labels.pkl
- eye_label.json
- year_label.json
- hair_label.json

After loading in labels.pkl with pickle, you will get a dictionary of { filname : labels }. The labels are formatted as (eye, hair, year) tuples.

    "32455": (8, 10, 5),

This means 32455.jpg has eye class 8, hair class 10, year class 5.

Missing labels will be a None. All images from dataset 1 will have year labels None, while all images from dataset 2 will have eye and hair label None.

Source code in the current repo is used to train on the first dataset. This requires some manual preprocessing (see dataset/ to extract the first dataset from the whole dataset.

The .json files map discrete labels to semantics.

// eye_label.json
    "aqua": 0, 
    "black": 1, 

Some notes

  • When training on the first dataset, adding some color transformations to images as a preprocessing step traning might help. You can achieve this through various torchvisions.transforms.functional methods.
  • Train with N(0, 1) but sample from Gaussian of smaller variance when evaluating. Just an engineering hack to get better results.

Anime Generation

🎨 Anime generation with GANs.

Anime Generation Info

⭐ Stars 46
🔗 Source Code
🕒 Last Update a month ago
🕒 Created 3 years ago
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😎 Author bchao1