This repo contains PyTorch model definitions, pre-trained weights and inference/sampling code for our paper exploring Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation. You can find more visualizations on our project page.
PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation
Junsong Chen*, Chongjian Ge*, Enze Xie*†, Yue Wu*, Lewei Yao, Xiaozhe Ren, Zhongdao Wang, Ping Luo, Huchuan Lu, Zhenguo Li
Huawei Noah’s Ark Lab, DLUT, HKU, HKUST
Learning from the previous PixArt-α project, we will try to keep this repo as simple as possible so that everyone in the PixArt community can use it.
- (🔥 New) Apr. 6, 2024. 💥 PixArt-Σ checkpoint 256px & 512px are released!
- (🔥 New) Mar. 29, 2024. 💥 PixArt-Σ training & inference code & toy data are released!!!
- Python >= 3.9 (Recommend to use Anaconda or Miniconda)
- PyTorch >= 2.0.0+cu11.7
conda create -n pixart python==3.9.0
conda activate pixart
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia
git clone https://github.com/PixArt-alpha/PixArt-sigma.git
cd PixArt-sigma
pip install -r requirements.txt
First of all.
We start a new repo to build a more user friendly and more compatible codebase. The main model structure is the same as PixArt-α, you can still develop your function base on the original repo. lso, This repo will support PixArt-alpha in the future.
Now you can train your model without prior feature extraction. We reform the data structure in PixArt-α code base, so that everyone can start to train & inference & visualize at the very beginning without any pain.
Download the toy dataset first. The dataset structure for training is:
cd ./pixart-sigma-toy-dataset
Dataset Structure
├──InternImgs/ (images are saved here)
│ ├──000000000000.png
│ ├──000000000001.png
│ ├──......
├──InternData/
│ ├──data_info.json (meta data)
Optional(👇)
│ ├──img_sdxl_vae_features_1024resolution_ms_new (run tools/extract_caption_feature.py to generate caption T5 features, same name as images except .npz extension)
│ │ ├──000000000000.npy
│ │ ├──000000000001.npy
│ │ ├──......
│ ├──caption_features_new
│ │ ├──000000000000.npz
│ │ ├──000000000001.npz
│ │ ├──......
│ ├──sharegpt4v_caption_features_new (run tools/extract_caption_feature.py to generate caption T5 features, same name as images except .npz extension)
│ │ ├──000000000000.npz
│ │ ├──000000000001.npz
│ │ ├──......
# SDXL-VAE, T5 checkpoints
git lfs install
git clone https://huggingface.co/PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers output/pixart_sigma_sdxlvae_T5_diffusers
# PixArt-Sigma checkpoints
python tools/download.py
Selecting your desired config file from config files dir.
python -m torch.distributed.launch --nproc_per_node=1 --master_port=12345 \
train_scripts/train.py \
configs/pixart_sigma_config/PixArt_sigma_xl2_img512_internalms.py \
--load-from output/pretrained_models/PixArt-Sigma-XL-2-512-MS.pth
--work-dir output/your_first_pixart-exp \
--debug
1. Quick start with Gradio
To get started, first install the required dependencies. Make sure you've downloaded the checkpoint files
from models(coming soon) to the output/pretrained_models
folder,
and then run on your local machine:
# SDXL-VAE, T5 checkpoints
git lfs install
git clone https://huggingface.co/PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers output/pixart_sigma_sdxlvae_T5_diffusers
# PixArt-Sigma checkpoints
python tools/download.py
# demo launch
python scripts/interface.py --model_path output/pretrained_models/PixArt-Sigma-XL-2-512-MS.pth --image_size 512 --port 11223
(Coming soon)
We will try our best to release
- Training code
- Inference code
- Model zoo
- Diffusers
- training & inference code of One Step Sampling with DMD
before 10th, April.