---
license: apache-2.0
pipeline_tag: unconditional-image-generation
---
# PixNerd: Pixel Neural Field Diffusion
PixNerd is a novel pixel-space diffusion transformer for image generation, introduced in the paper [PixNerd: Pixel Neural Field Diffusion](https://huggingface.co/papers/2507.23268). Unlike conventional diffusion models that depend on a compressed latent space shaped by a pre-trained VAE, PixNerd proposes to model patch-wise decoding with a neural field. This results in a single-scale, single-stage, efficient, and end-to-end solution that directly operates in pixel space, avoiding accumulated errors and decoding artifacts.
### ✨ Key Highlights
* **Efficient Pixel-Space Diffusion**: Directly models image generation in pixel space, eliminating the need for VAEs and their associated complexities or artifacts.
* **Neural Field Decoding**: Employs neural fields for patch-wise decoding, improving the modeling of high-frequency details.
* **Single-Stage & End-to-End**: Offers a simplified, efficient training and inference paradigm without complex cascade pipelines.
* **High Performance**: Achieves competitive FID scores on ImageNet 256x256 (2.15 FID) and 512x512 (2.84 FID) for unconditional image generation.
* **Text-to-Image Extension**: The framework is extensible to text-to-image applications, achieving strong results on benchmarks like GenEval (0.73 overall score) and DPG (80.9 overall score).
## Visualizations
Below are sample images generated by PixNerd, showcasing its capabilities:
## Checkpoints
The following checkpoints are available:
| Dataset | Model | Params | FID | HuggingFace |
|---------------|---------------|--------|-------|---------------------------------------|
| ImageNet256 | PixNerd-XL/16 | 700M | 2.15 | [🤗](https://huggingface.co/MCG-NJU/PixNerd-XL-P16-C2I) |
| ImageNet512 | PixNerd-XL/16 | 700M | 2.84 | [🤗](https://huggingface.co/MCG-NJU/PixNerd-XL-P16-C2I) |
| Dataset | Model | Params | GenEval | DPG | HuggingFace |
|---------------|---------------|--------|------|------|----------------------------------------------------------|
| Text-to-Image | PixNerd-XXL/16| 1.2B | 0.73 | 80.9 | [🤗](https://huggingface.co/MCG-NJU/PixNerd-XXL-P16-T2I) |
## Online Demos
You can try out the PixNerd-XXL/16 (text-to-image) model on our Hugging Face Space demo: [https://huggingface.co/spaces/MCG-NJU/PixNerd](https://huggingface.co/spaces/MCG-NJU/PixNerd).
To host a local Gradio demo for text-to-image applications, run the following command after setting up the environment:
```bash
python app.py --config configs_t2i/inference_heavydecoder.yaml --ckpt_path=XXX.ckpt
```
## Usage
For image generation (C2i for ImageNet), you can use the provided codebase. First, install the required dependencies:
```bash
# for installation
pip install -r requirements.txt
```
Then, run inference using the `main.py` script (replace `XXX.ckpt` with your checkpoint path):
```bash
# for inference
python main.py predict -c configs_c2i/pix256std1_repa_pixnerd_xl.yaml --ckpt_path=XXX.ckpt
# or specify the GPU(s) to use:
CUDA_VISIBLE_DEVICES=0,1, python main.py predict -c configs_c2i/pix256std1_repa_pixnerd_xl.yaml --ckpt_path=XXX.ckpt
```
For more details on training and evaluation for both C2i and T2i applications, please refer to the [official GitHub repository](https://github.com/MCG-NJU/PixNerd).
## Citation
If you find this work useful for your research, please cite our paper:
```bibtex
@article{2507.23268,
Author = {Shuai Wang and Ziteng Gao and Chenhui Zhu and Weilin Huang and Limin Wang},
Title = {PixNerd: Pixel Neural Field Diffusion},
Year = {2025},
Eprint = {arXiv:2507.23268},
}
```
## Acknowledgement
The code is mainly built upon [FlowDCN](https://github.com/MCG-NJU/DDT) and [DDT](https://github.com/MCG-NJU/FlowDCN).
