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license: mit
library_name: diffusers
tags:
- diffusers
- image-generation
- class-conditional
- imagenet
- pixnerd
language:
- en
---
# PixNerd-XL-16 Diffusers Checkpoints
Production-ready Diffusers export of PixNerd-XL/16 class-conditional ImageNet checkpoints.
## Available Checkpoints
- `PixNerd-XL-16-256`
- source: `epoch%3D319-step%3D1600000_emainit.ckpt`
- target resolution: `256x256`
- `PixNerd-XL-16-512`
- source: `res512_ft200k_epoch%3D325-step%3D1800000_emainit.ckpt`
- target resolution: `512x512`
Both checkpoints are packaged with:
- `pipeline.py`
- `modeling_pixnerd_transformer_2d.py`
- `scheduling_pixnerd_flow_match.py`
- `transformer/` weights + config
- `scheduler/` config
## Requirements
```bash
pip install torch diffusers
```
## Inference (Python)
```python
import torch
from diffusers import DiffusionPipeline
model_dir = "PixNerd-XL-16-256" # or PixNerd-XL-16-512
pipe = DiffusionPipeline.from_pretrained(
model_dir,
custom_pipeline=f"{model_dir}/pipeline.py",
torch_dtype=torch.float32,
).to("cpu") # use "cuda" if available
# Class-conditional generation: class label 207 (golden retriever)
images = pipe(
prompt=[207],
num_images_per_prompt=1,
height=256,
width=256,
num_inference_steps=25,
guidance_scale=4.0,
timeshift=3.0,
order=2,
).images
images[0].save("sample.png")
```
## Interface Notes
- The pipeline uses `prompt` for conditioning input.
- For class-conditional generation, pass integer labels, e.g. `prompt=[207]`.
- `height` and `width` should match checkpoint intent (256 or 512), but custom sizes work if divisible by patch size.
## Reproducibility Metadata
- Architecture and conversion provenance are recorded in each checkpoint's `conversion_metadata.json`.
- Transformer and scheduler runtime classes are defined in repository-local Python modules shipped with each checkpoint.
## Limitations
- Intended for ImageNet class-conditional generation.
- No text encoder is included.
- Output quality depends on scheduler settings and inference step count.
## Citation
Source paper (ICLR 2026):
- [PixNerd: Pixel Neural Field Diffusion](http://arxiv.org/abs/2507.23268)
- [Hugging Face Papers page](https://huggingface.co/papers/2507.23268)
Source code:
- Original PixNerd codebase: [MCG-NJU/PixNerd](https://github.com/MCG-NJU/PixNerd)
- Diffusers conversion code used for this export: [Bili-Sakura/PixNerd-diffusers](https://github.com/Bili-Sakura/PixNerd-diffusers)
```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},
}
```
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