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MUSE-Pytorch🎨

This is a PyTorch implementation of MUSE with pre-trained checkpoints on ImageNet and CC3M.

Unlike the original cross-attention conditioning type, we employ an in-context conditioning version of MUSE and adopt the recently proposed U-ViT for its high performance in image generation. A text-to-image version of our implemented pipeline is illustrated below:

image-20230505101233172

Note:

  1. Due to computational constraints, the released models are notably undertrained. Nonetheless, they can already achieve satisfactory performance, and we release them to facilitate community research. One can also resume training from the released checkpoints for better results.

  2. The core functionality of MUSE is implemented in the MUSE class (which is only ~60 lines) in libs/muse.py.

Pretrained Models

The pre-trained models are released in 🤗HuggingFace, the detailed information is shown below:

Dataset Model #Params #Training iterations Batch size FID
ImageNet 256x256 U-ViT-B (depth=13, width=768) 102M 450K 2048 3.84 (12 steps)
CC3M U-ViT-Huge (depth=29, width=1152) 501M 285K 2048 6.84 (18 steps)

Dependencies

conda install pytorch torchvision torchaudio cudatoolkit=11.3
pip install accelerate==0.12.0 absl-py ml_collections einops wandb ftfy==6.1.1 transformers==4.23.1 loguru webdataset==0.2.5

Data Preparation

First, download VQGAN from this link (from MAGE, thanks!), and put the downloaded VQGAN in assets/vqgan_jax_strongaug.ckpt.

  • ImageNet 256x256: Extract ImageNet features by running: python extract_imagenet_feature.py your/imagenet/path
  • CC3M:
    • First, prepare some context features for training by running python extract_test_prompt_feature.py and python extract_empty_feature.py
    • Next, prepare the webdataset feature2webdataset.py

Training & Evaluation

Download the reference statistics for FID from this link. Place the downloaded .npz file in assets/fid_stats.

Next, download the pre-trained checkpoints from this link to assets/ckpts for evaluation or to continue training for more iterations.

ImageNet 256x256 (class-conditional)

# export EVAL_CKPT="assets/ckpts/imagenet256-450000.ckpt"  # uncomment this to perform evaluation. Otherwise, perform training.
export OUTPUT_DIR="output_dir/for/this/experiment"
mkdir -p $OUTPUT_DIR

accelerate launch --num_processes 8 --mixed_precision fp16 train_t2i_discrete_muse.py \
 --config=configs/imagenet256_base_vq_jax.py

Expected evaluation results:

step=450000 fid50000=3.8392620678172307

CC3M (text-to-image)

# export EVAL_CKPT="assets/ckpts/cc3m-285000.ckpt"  # uncomment this to perform evaluation. Otherwise, perform training.
export OUTPUT_DIR="output_dir/for/this/experiment"
mkdir -p $OUTPUT_DIR

accelerate launch --num_processes 8 --mixed_precision fp16 train_t2i_discrete_wds.py \
--config=configs/cc3m_xl_vqf16_jax_2048bs_featset_CLIP_G.py

Expected evaluation results:

step=285000 fid30000=6.835978505261096

The generated images are stored in OUTPUT_DIR/eval_samples. Each time the script is executed, a sub-directory with timestamp will be created to store the generated images.

This implementation is based on

Thanks to all authors for their work!