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# 论文复现：Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

English|简体中文

1 Introduction

This project is based on the pixel2style2pixel (pSp). pSp framework generates a series of style vectors based on a novel encoder network, which is fed into a pre-trained style generator to form an extended W + potential space. The encoder can directly reconstruct real input images.

Paper

  • [1] Richardson E , Alaluf Y , Patashnik O , et al. Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation[J]. 2020.

Reference project

Project on Ai Studio

2 Result

Results（Test on CelebA-HQ）

Model LPIPS Similarity MSE
Paper 0.17 0.56 0.03
Pytorch 0.15 0.57 0.03
Paddle 0.17 0.57 0.03

Visual comparison

Pytorch Paddle
1 1
1 1
1 1

3 Datasets

4 Environment

Hardware：GPU、CPU

Framework：PaddlePaddle >=2.0.0

5 Pretrained models

Pretrained models saved inpretrained_models/.

Pretrained models Description
FFHQ StyleGAN(stylegan2-ffhq-config-f.pdparams) StyleGAN trained with the FFHQ dataset fromrosinality ，output size:1024x1024
IR-SE50 Model(model_ir_se50.pdparams) IR_SE model (TreB1eN)trained for computering ID loss.
CurricularFace Backbone(CurricularFace_Backbone.paparams) Pretrained CurricularFace model(HuangYG123)evaled Similarity
AlexNet(alexnet.pdparams和lin_alex.pdparams) computered lpips loss
StyleGAN Inversion(psp_ffhq_inverse.pdparams) pSp trained with the FFHQ dataset for StyleGAN inversion.

Baidu driver：https://pan.baidu.com/s/1G-Ffs8-y93R0ZlD9mEU6Eg password：m3nb

Pretrained pSp encoder：

模型 Description
StyleGAN Inversion(psp_ffhq_inverse.pdparams) pSp trained with the FFHQ dataset for StyleGAN inversion.

6 Quick start

Compile operation

python scripts/compile_ranger.py

Train

python scripts/train.py \
--dataset_type=ffhq_encode \
--exp_dir=exp/test \
--workers=0 \
--batch_size=8 \
--test_batch_size=8 \
--test_workers=0 \
--val_interval=2500 \
--save_interval=5000 \
--encoder_type=GradualStyleEncoder \
--start_from_latent_avg \
--lpips_lambda=0.8 \
--l2_lambda=1 \
--id_lambda=0.1 \
--optim_name=ranger

inferernce

python scripts/inference.py \
--exp_dir=inference \
--checkpoint_path=pretrained_models/psp_ffhq_inverse.pdparams \
--data_path=CelebA_test \
--test_batch_size=8 \
--test_workers=4

Others

  • LPIPS
python scripts/calc_losses_on_images.py \
--mode lpips \
--data_path=inference/inference_results \
--gt_path=CelebA_test
  • MSE
python scripts/calc_losses_on_images.py \
--mode l2 \
--data_path=inference/inference_results \
--gt_path=CelebA_test
  • Similarity
python scripts/calc_id_loss_parallel.py \
--data_path=inference/inference_results \
--gt_path=CelebA_test

7 Code structure

Structure

├─config          # 配置
    ├─data            #数据集加载
       ├─CelebA_test  # 测试数据图像
    ├─logs            #日志
       ├─train        # 训练日志
       ├─test         # 测试日志
    ├─models          # 模型
        ├─encoders    # 编码器
        ├─loss        # 损失函数
        ├─mtcnn       #     
        ├─stylegan2   #       
        ├─utils       # 编译算子
    ├─scripts         #算法执行
        trian         #训练
        inference     #测试
    ├─utils           # 工具代码
    │  README.md      #英文readme
    │  README_cn.md   #中文readme

Parameter description

Parameter Default
Config None

8 Model information

The overall information of the model is as follows:

Information Descriptions
Version Paddle 2.1.2
Application Image Generation
Hardware GPU / CPU

License

# encoding=utf8
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.