PixelDiT-1300M-1024px

<p align="center"> <img src="https://raw.githubusercontent.com/NVlabs/PixelDiT/master/assets/pixeldit-logo.png" height="60" /> </p> <h2 align="center">PixelDiT: Pixel Diffusion Transformers for Image Generation</h2> <p align="center"> <a href="https://www.yongshengyu.com/">Yongsheng Yu</a>^1,2 &nbsp; <a href="https://wxiong.me/">Wei Xiong</a>^1† &nbsp; <a href="https://weilinie.github.io/">Weili Nie</a>¹ &nbsp; <a href="https://shengcn.github.io/">Yichen Sheng</a>¹ &nbsp; <a href="http://behindthepixels.io/">Shiqiu Liu</a>¹ &nbsp; <a href="https://www.cs.rochester.edu/u/jluo/">Jiebo Luo</a>² </p> <p align="center"> ¹NVIDIA &nbsp; ²University of Rochester <br> ^†Project Lead and Main Advising </p> <p align="center"> <a href="https://pixeldit.github.io/"><img src="https://img.shields.io/badge/Website-Project_Page-2ea44f" /></a> &nbsp; <a href="https://arxiv.org/abs/2511.20645"><img src="https://img.shields.io/badge/arXiv-2511.20645-b31b1b.svg" /></a> &nbsp; <a href="https://github.com/NVlabs/PixelDiT"><img src="https://img.shields.io/badge/GitHub-Code-blue" /></a> </p>

Key Features

• VAE-free
• Dual-level architecture: Patch-level DiT + Pixel-level DiT
• MM-DiT text-image fusion: Joint attention between text and image tokens
• Text encoder: Gemma-2-2B-IT
• Multi-aspect-ratio: Supports various aspect ratios at 1024px

Usage

Installation

pip install -r requirements.txt

Inference

# See the full inference script at: https://github.com/NVlabs/PixelDiT
cd t2i/
python inference.py \
  --config configs/PixelDiT_1024px_pixel_diffusion_stage3.yaml \
  --model_path PixelDiT-T2I-v1.pth \
  --txt_file prompts.txt \
  --custom_height 1024 --custom_width 1024 \
  --cfg_scale 2.75 --seed 2025 \
  --negative_prompt "low quality, worst quality, over-saturated, blurry, deformed, watermark" \
  --work_dir "."

Inference Parameters

Model Architecture

Citation

@inproceedings{yu2026pixeldit,
      title={PixelDiT: Pixel Diffusion Transformers for Image Generation},
      author={Yongsheng Yu and Wei Xiong and Weili Nie and Yichen Sheng and Shiqiu Liu and Jiebo Luo},
      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
      year={2026},
}

License

This model is released under the NSCLv1 License. The work and any derivative works may only be used for non-commercial (research or evaluation) purposes.