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

   

Pre-trained Checkpoints

All evaluations use FlowDPMSolver with 100 steps. 50K samples. Metrics follow the ADM evaluation protocol.

Usage

Installation

pip install -r requirements.txt

Evaluation (Generate 50K Samples)

cd c2i/

# ImageNet 256x256 (epoch 320, best FID)
torchrun --nproc_per_node=8 main.py predict \
  -c configs/pix256_xl.yaml \
  --ckpt_path=imagenet256_pixeldit_xl_epoch320.ckpt \
  --model.diffusion_sampler.class_path=src.diffusion.FlowDPMSolverSampler \
  --model.diffusion_sampler.init_args.num_steps=100 \
  --model.diffusion_sampler.init_args.guidance=2.75 \
  --model.diffusion_sampler.init_args.timeshift=1.0 \
  --model.diffusion_sampler.init_args.guidance_interval_min=0.1 \
  --model.diffusion_sampler.init_args.guidance_interval_max=0.9 \
  --per_run_seed=false --seed_everything=1000

# ImageNet 512x512
torchrun --nproc_per_node=8 main.py predict \
  -c configs/pix512_xl.yaml \
  --ckpt_path=imagenet512_pixeldit_xl.ckpt \
  --model.diffusion_sampler.class_path=src.diffusion.FlowDPMSolverSampler \
  --model.diffusion_sampler.init_args.num_steps=100 \
  --model.diffusion_sampler.init_args.guidance=3.5 \
  --model.diffusion_sampler.init_args.timeshift=2.0 \
  --model.diffusion_sampler.init_args.guidance_interval_min=0.1 \
  --model.diffusion_sampler.init_args.guidance_interval_max=1.0 \
  --per_run_seed=false --seed_everything=10000

After generating samples, compute FID with the ADM evaluation toolkit.

Citation

@inproceedings{yu2025pixeldit,
      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.