huynt654 / ctm

[ICLR'24] Consistency Trajectory Model (CTM)

This repository houses the official PyTorch implementation of the paper titled "Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion" on ImageNet 64x64, which is presented at ICLR 2024.

• arXiv
• Project Page
• OpenReview
• Codes of other datasets (to be released...)

Contacts:

• Dongjun KIM: [email protected]
• Chieh-Hsin (Jesse) LAI: [email protected]

TL;DR

For single-step diffusion model sampling, our new model, Consistency Trajectory Model (CTM), achieves SOTA on CIFAR-10 (FID 1.73) and ImageNet 64x64 (FID 1.92). CTM offers diverse sampling options and balances computational budget with sample fidelity effectively.

Checkpoints

• Download and put the checkpoints in the file of author_ckpt
• CTM checkpoint on ImageNet64 (ema=0.999)

Prereqruisites

1. Download (or obtain) the following files

   • Pretrained diffusion model: Please locate it in args.teacher_model_path
   • Data: Please locate it in args.data_dir (Note that the data we use is NOT the downsampled image data. It is ILSVRC2012 data. There are huge performance gap between those two datasets.)
   • Reference statistics: statistics for computing FID, sFID, IS, precision, recall. Please locate them in args.ref_path

2. Install docker to your own server

   2-1. Type docker pull dongjun57/ctm-docker:latest to download docker image in docker hub.

   2-2. Create a container by typing in the command: docker run --gpus=all -itd -v /etc/localtime:/etc/localtime:ro -v /dev/shm:/dev/shm -v [specified directory]:[specified directory] -v /hdd/imagenet/imagenet_dir/train:/hdd/imagenet/imagenet_dir/train -v [specified data directory]:[specified data directory] --name ctm-docker 8caa2682d007 The commands could vary by your server environment.

   2-3. Go to the container by docker exec -it ctm-docker bash.

   2-4. Go to the virtual environment by conda activate ctm.

3. Make sure the dependencies consistent with the following.

   apt install git
   apt install libopenmpi-dev
   python -m pip install tensorflow[and-cuda]
   python -m pip install torch torchvision torchaudio
   python -m pip install blobfile tqdm numpy scipy pandas Cython piq==0.7.0
   python -m pip install joblib==0.14.0 albumentations==0.4.3 lmdb clip@git+https://github.com/openai/CLIP.git pillow
   python -m pip install flash-attn --no-build-isolation
   python -m pip install xformers
   python -m pip install mpi4py
   python -m pip install nvidia-ml-py3 timm==0.4.12 legacy dill nvidia-ml-py3
   

Training

• For CTM+DSM training, run bash commands/CTM+DSM_command.sh

  Recommendation: at least run CTM+DSM for 10~50k iterations

• For CTM+DSM+GAN training, run bash commands/CTM+DSM+GAN_command.sh

  Recommendation: at least run CTM+DSM+GAN for >=30k iterations

Sampling

Please see commands/sampling_commands.sh for detailed sampling commands.

Evaluating

Run python3.8 evaluations/evaluator.py [location_of_statistics] [location_of_samples]

The first argument is the reference path and the second argument is the folder of your samples (>=50k samples for correct evaluation).

Please refer to the statistics of ADM (Prafulla Dhariwal, Alex Nichol).

Customized dataset

Users need to manually replace the data_name with your data name: manually modify the data_name in cm_train.py or image_sample.py

Citations

@article{kim2023consistency,
  title={Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion},
  author={Kim, Dongjun and Lai, Chieh-Hsin and Liao, Wei-Hsiang and Murata, Naoki and Takida, Yuhta and Uesaka, Toshimitsu and He, Yutong and Mitsufuji, Yuki and Ermon, Stefano},
  journal={arXiv preprint arXiv:2310.02279},
  year={2023}