Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis (Self-Flow) [ICML'26]

Hila Chefer* · Patrick Esser*
Dominik Lorenz · Dustin Podell · Vikash Raja · Vinh Tong · Antonio Torralba · Robin Rombach
Black Forest Labs

This folder contains inference code for generating images with our Self-Flow trained diffusion model on ImageNet 256×256.

Overview

Self-Flow (Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis) is a training framework that combines the flow matching objective with a self-supervised feature reconstruction objective.

This inference code allows you to:

1. Load a Self-Flow checkpoints (pretrained on ImageNet 256x256)
2. Generate 50,000 images for FID evaluation

The generated samples can be evaluated using the ADM evaluation suite.

Requirements

pip install -r requirements.txt

Quick Start

Download Checkpoint

python -c "
from huggingface_hub import hf_hub_download
hf_hub_download(
    repo_id='Hila/Self-Flow',
    filename='selfflow_imagenet256.pt',
    local_dir='./checkpoints'
)
print('Downloaded!')
"

Generate 50k samples (multi-GPU recommended)

torchrun --nnodes=1 --nproc_per_node=8 sample.py \
    --ckpt checkpoints/selfflow_imagenet256.pt \
    --output-dir ./samples \
    --num-fid-samples 50000

Single GPU

python sample.py \
    --ckpt checkpoints/selfflow_imagenet256.pt \
    --output-dir ./samples \
    --num-fid-samples 50000 \
    --batch-size 64

Command Line Arguments

Argument	Default	Description
--ckpt	required	Path to model checkpoint
--output-dir	./samples	Output directory for generated samples
--num-fid-samples	50000	Number of samples to generate
--batch-size	64	Batch size per GPU
--num-steps	250	Number of diffusion sampling steps
--mode	SDE	Sampling mode: SDE or ODE
--seed	31	Random seed for reproducibility
--cfg-scale	1.0	Classifier-free guidance scale (1.0 = no guidance, as used in paper)

Evaluation

The generated .npz file can be used with the ADM evaluation suite to compute FID, IS, Precision, and Recall.

Download Reference Statistics

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

Run Evaluation

python evaluator.py \
    VIRTUAL_imagenet256_labeled.npz \
    ./samples/samples_50000.npz ./samples

Model Architecture

The Self-Flow model is based on SiT-XL/2 with the following specifications

A key architectural modification is per-token timestep conditioning, which allows each token to have a different noise level during training.

Project Structure

Self-Flow/
├── sample.py           # Main sampling script
├── checkpoints/        # Place model checkpoints here
├── requirements.txt    # Python dependencies
├── README.md           # This file
└── src/                # Model and sampling implementations
    ├── model.py        # SelfFlowPerTokenDiT model
    ├── sampling.py     # Diffusion sampling utilities
    └── utils.py        # Position encoding utilities

Training Details

The model was trained using the following configuration:

• Model: SiT-XL/2 with per-token timestep conditioning
• Training: Self-Flow with per-token masking (25% mask ratio)
• Optimizer: AdamW with gradient clipping (max_norm=1)
• Mixed precision: BFloat16
• Self-distillation: Teacher at layer 20 (EMA), student at layer 8

Acknowledgments

This code builds upon:

• REPA - Representation Alignment for Generation
• SiT - Scalable Interpolant Transformers

BibTeX

If you use this work, please cite:

@article{CheferEsser2026selfflow,
      title={Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis}, 
      author={Hila Chefer and Patrick Esser and Dominik Lorenz and Dustin Podell and Vikash Raja and Vinh Tong and Antonio Torralba and Robin Rombach},
      journal = {arXiv preprint arXiv:2603.06507},
      year={2026},
}