SDE-RAE

IMAGE REC AND EDIT

Code and dataset coming soon

Project | [Paper](coming soon)

SDE-RAE

PyTorch implementation of SDE-RAE

Quickstart

Follow the instructions below to download and run SDE-RAE on your own local. These instructions have been tested on a GPU with >18GB VRAM. If you don't have a GPU, you may need to change the default configuration.

Set up a conda environment, and download a pretrained model:

Pytorch 1.9.1, Python 3.8

conda env create -n sde_rae
conda activate sde_rae
pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
pip install ftfy regex tqdm
pip install git+https://github.com/openai/CLIP.git
pip install matplotlib numpy PyYAML tensorboard tqdm

pytorch

Getting Started

The code will automatically download pretrained SDE (VP) PyTorch models on CelebA-HQ, LSUN bedroom

Re-training the model

Here is the PyTorch implementation for training the model.

dataset

Here is the dataset celeba-HQ And lsun

pretrained

download pretrained pretrained 提取码：k8gb

unzip to "./pretrained"

TRAINING:

Download clip-encoder, unzip it to model_fast clip-encoder提取码：7gyz

python train_fast.py --content_dir ./datasets/celeba_train --npy_name celeba --num_test 16 --decoder ./model_fast/clip_decoder_pencil.pth.tar

TESTING:

python test_fast.py --content_dir ./datasets/celeba_test --npy_name celeba --config celeba.yml --max_iter 10000 --batch_size 4

method:

... REC ... ... EDIT ...

Acknowledgements

Score-Based Generative Modeling through Stochastic Differential Equations sde

SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations SDEITE

High-Fidelity GAN Inversion for Image Attribute Editing HFGI

Learning Transferable Visual Models From Natural Language Supervision cliP

StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery (ICCV 2021 Oral)StyleCLIP

StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generatorsstylegan-nada

One-Shot Adaptation of GAN in Just One CLIP (TPAMI)OneshotCLIP

VQGAN-CLIPvqgan-clip