MeanFlow Transformers with Representation Autoencoders (RAE)
_{Official PyTorch Implementation}

Paper

This repository is based on:

• Diffusion Transformers with Representation Autoencoders.
• and our previous work, CMT: Consistency Mid-Training.

Environment

Dependency Setup

1. Create environment and install via uv:

   conda create -n rae python=3.10 -y
   conda activate rae
   pip install uv
   
   # Install PyTorch 2.2.0 with CUDA 12.1
   uv pip install torch==2.2.0 torchvision==0.17.0 torchaudio --index-url https://download.pytorch.org/whl/cu121
   
   # Install other dependencies
   uv pip install timm==0.9.16 accelerate==0.23.0 torchdiffeq==0.2.5 wandb
   uv pip install "numpy<2" transformers einops omegaconf
   

Data Preparation

1. Download ImageNet-1k raw data without preprocessing.
2. Point Stage 1 and Stage 2 scripts to the training split via --data-path.

Pre-Training: Flow Matching

The RAE authors release flow-matching pre-traine models: RAE decoders, DiT^DH diffusion transformers and stats for latent normalization. To download all models at once:

cd RAE
pip install huggingface_hub
hf download nyu-visionx/RAE-collections \
  --local-dir models 

To download specific models, run:

hf download nyu-visionx/RAE-collections \
  <remote_model_path> \
  --local-dir models 

Consistent Mid-Training

bash CMT_256.sh

bash CMT_512.sh

MFT and MFD Post-Training

Make sure to input the CMT checkpoint path obtained from the previous stage.

For instance, on ImageNet 512, they are

bash MFT_512.sh

bash MFD_512.sh

Distributed sampling for evaluation

Make sure to input the MeanFlow-RAE checkpoint path after training to the config file.

We provide our trained MF-RAE on Google Drive: https://drive.google.com/drive/folders/1EYVyIDKRZeHn6NO7uF5aJ1ycR3lvfnJu?usp=drive_link

bash Sample_256.sh

bash Sample_512.sh

Evaluation

ADM Suite FID setup

Use the ADM evaluation suite to score generated samples:

1. Clone the repo:

   git clone https://github.com/openai/guided-diffusion.git
   cd guided-diffusion/evaluation
   

2. Create an environment and install dependencies:

   conda create -n adm-fid python=3.10
   conda activate adm-fid
   pip install 'tensorflow[and-cuda]'==2.19 scipy requests tqdm
   

3. Download ImageNet statistics (256×256 shown here):

   wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/VIRTUAL_imagenet256_labeled.npz
   

4. Evaluate:

   python evaluator.py VIRTUAL_imagenet256_labeled.npz /path/to/samples.npz
   

Acknowledgement

This code is built upon the following repositories:

• SiT - for diffusion implementation and training codebase.
• DDT - for some of the DiT^DH implementation.
• LightningDiT - for the PyTorch Lightning based DiT implementation.
• MAE - for the ViT decoder architecture.
• RAE - for the RAE model and checkpoints.