few_shot_gan_adaption

Few-shot Image Generation via Cross-domain Correspondence

Paper: https://arxiv.org/abs/2104.06820
Repository: https://github.com/utkarshojha/few-shot-gan-adaptation

Table of Contents

• 1. Basic Usage
• 2. Checkpoints
• 3. Documentation
• 4. Training
• 5. Images
• 6. License

1. Basic Usage

import jax
import numpy as np
import dill as pickle
from PIL import Image

import flaxmodels as fm

ckpt = pickle.load(open('sketches.pickle', 'rb'))
params = ckpt['params_ema_G']

generator = fm.few_shot_gan_adaption.Generator()

# Seed
key = jax.random.PRNGKey(0)

# Input noise
z = jax.random.normal(key, shape=(4, 512))

# Generate images
images, _ = generator.apply(params, z, truncation_psi=0.5, train=False, noise_mode='const')

# Normalize images to be in range [0, 1]
images = (images - np.min(images)) / (np.max(images) - np.min(images))

# Save images
for i in range(images.shape[0]):
    Image.fromarray(np.uint8(images[i] * 255)).save(f'image_{i}.jpg')

2. Checkpoints

• Sketches (357,2 MB)
• Amedeo Modigliani (357,2 MB)
• Babies (357,2 MB)
• Otto Dix (357,2 MB)
• Rafael (357,2 MB)

3. Documentation

The documentation can be found here.

4. Training

If you want to train this model in Jax/Flax, go here.

5. Images

Sketches

Amedeo Modigliani

Babies

Otto Dix

Rafael

6. License

MIT License