Feature Guided Flow

Implementation of "Improved Image Generation of Normalizing Flow with Semantic Feature Guidance" (TBA) in Pytorch

Architecture

Requirements

CUDA 11.0
Python 3.9
PyTorch 1.11
others : requirements.txt

git clone https://github.com/dajinstory/feature-guided-flow.git
cd feature-guided-flow
pip install -r requirements.txt

Usage

Preparing Dataset, Configs

For training, you need to prepare Dataset and meta file. Meta file for CelebA dataset are in data/{DATASET_NAME}/train_meta.csv. It only contains the file name of dataset.

Also you should edit config files. There are "*_path" named keys. Those keys contains root path of dataset, path of meta file and path to save log and checkpoint files.

Training Model

You can train model from scratch,

CUDA_VISIBLE_DEVICES=0 python src/train.py --config config/train/fgflow_v0_fg_recon.yml

Demo

You can check the sampling result of the pretrained model by running demo/demo.ipynb

If you want to utilize the FGFlow model for your personal research, you just need to refer to src/ folder and demo/ folder.

Pretrained Checkpoints

I trained 64x64 models on CelebA dataset for ???? iterations. The model followed the setting from GLOW official paper. I got bpd(bits per dimension) about ??, . I trained 64x64 model with 1 GPU, 16 mini-batch size on each GPU.

HParam	FGFlow64x64V0
input shape	(64,64,3)
L	4
K	48
hidden_channels	512
flow_permutation	invertible 1x1 conv
flow_coupling	affine
batch_size	64 on each GPU, with 1 GPUs
y_condition	false

Model	Dataset	Checkpoint	Note
FGFlow64x64V0	CelebA	FGFlow64X64V0_CelebA	64x64 CelebA Dataset
FGFlow256x256V0	CelebA	FGFlow256X256V0_CelebA	256x256 CelebA Dataset

Samples

Generated samples. Left to right: Results by GLOW and Ours. We can see more details (e.g., hair, expression) in our results than in the GLOW baseline.

Result of z space interpolation. Up to down: Results by GLOW and Ours.

Reference

https://github.com/dajinstory/glow-pytorch

@software{NF-pytorch,
  author = {Dajin Han},
  title = {{Framework for Flow-based Model in Pytorch}},
  year = {2022},
  url = {http://github.com/dajinstory/glow-pytorch}
}