DyPE: Dynamic Position Extrapolation for Ultra High Resolution Diffusion

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TL;DR

DyPE (Dynamic Position Extrapolation) enables pre-trained diffusion transformers to generate ultra-high-resolution images far beyond their training scale. It dynamically adjusts positional encodings during denoising to match evolving frequency content—achieving faithful 4K × 4K results without retraining or extra sampling cost.

<div align="center"> <img src="docs/collage.png" alt="DyPE Results" width="100%"> </div>

Installation

Create a conda environment and install dependencies:

conda create -n dype python=3.10
conda activate dype
pip install -r requirements.txt

Usage

Generate ultra-high resolution images with DyPE using the run_dype.py script:

python run_dype.py --prompt "Your text prompt here"

Key Arguments:

| Argument | Default | Description | |----------|---------|-------------| | --prompt | Dark fantasy scene | Text prompt for image generation | | --height | 4096 | Image height in pixels | | --width | 4096 | Image width in pixels | | --steps | 28 | Number of inference steps | | --seed | 42 | Random seed for reproducibility | | --method | yarn | Position encoding method: yarn, ntk, or base | | --no_dype | False | Disable DyPE (enabled by default) |

Examples:

# Generate 4K image with default settings (YARN + DyPE)
python run_dype.py --prompt "A serene mountain landscape at sunset"

# Use NTK method without DyPE
python run_dype.py --method ntk --no_dype --prompt "A futuristic city skyline"

# Baseline comparison (no position encoding modifications)
python run_dype.py --method base

Generated images will be saved to the outputs/ folder (created automatically).

For Qwen-Image generation, use the run_dype_qwen.py script. It operates similarly to the standard script but defaults to the Qwen model architecture.

License and Commercial Use

This work is patent pending. For commercial use or licensing inquiries, please contact the authors.

Citation

If you find this useful for your research, please cite the following:

@misc{issachar2025dypedynamicpositionextrapolation,
      title={DyPE: Dynamic Position Extrapolation for Ultra High Resolution Diffusion}, 
      author={Noam Issachar and Guy Yariv and Sagie Benaim and Yossi Adi and Dani Lischinski and Raanan Fattal},
      year={2025},
      eprint={2510.20766},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2510.20766}, 
}