👁 Image

PixelDiT: Pixel Diffusion Transformers for Image Generation

Yongsheng Yu^1,2   Wei Xiong^1†   Weili Nie¹   Yichen Sheng¹   Shiqiu Liu¹   Jiebo Luo²

¹NVIDIA   ²University of Rochester
^†Project Lead and Main Advising

👁 Image
  👁 Image
  👁 Image

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.