Improve model card for PixNerd

This PR significantly enhances the model card for PixNerd.

It adds:
- The `pipeline_tag: text-to-image` and `library_name: transformers` to the metadata, ensuring better discoverability and indicating loading compatibility.
- Direct links to the Hugging Face paper page, the project's Hugging Face Space, and the GitHub repository.
- A comprehensive model description, key features, visualizations, checkpoint information, and detailed usage examples (including installation, inference, and training commands) directly sourced from the project's GitHub README.
- The official BibTeX citation and acknowledgements.

This update makes the model card much richer and more user-friendly, providing all necessary information in one place.

README.md

@@ -1,5 +1,92 @@
 ---
 license: apache-2.0
+pipeline_tag: text-to-image
+library_name: transformers
 ---
 
-arxiv.org/abs/2507.23268
+# PixNerd: Pixel Neural Field Diffusion
+
+<div style="text-align: center;">
+  <a href="https://huggingface.co/papers/2507.23268"><img src="https://img.shields.io/badge/arXiv-2507.23268-b31b1b.svg" alt="arXiv"></a>
+    <a href="https://huggingface.co/spaces/MCG-NJU/PixNerd"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Online_Demo-green" alt="arXiv"></a>  
+</div>
+
+![](https://huggingface.co/MCG-NJU/PixNerd-XL-P16-C2I/resolve/main/figs/arch.png)
+
+## Introduction
+The current success of diffusion transformers heavily depends on the compressed latent space shaped by the pre-trained variational autoencoder (VAE). However, this two-stage training paradigm inevitably introduces accumulated errors and decoding artifacts. To address these problems, we propose PixNerd: Pixel Neural Field Diffusion, a single-scale, single-stage, efficient, end-to-end solution for image generation.
+
+PixNerd is a powerful and efficient **pixel-space** diffusion transformer that directly operates without a VAE. It employs a neural field to model patch-wise decoding, improving high-frequency modeling.
+
+### Key Highlights
+*   **VAE-Free Pixel Space Generation**: Operates directly in pixel space, eliminating accumulated errors and decoding artifacts often introduced by VAEs.
+*   **High-Fidelity Image Synthesis**: Achieves competitive FID scores on ImageNet benchmarks:
+    *   **2.15 FID** on ImageNet $256\times256$ with PixNerd-XL/16.
+    *   **2.84 FID** on ImageNet $512\times512$ with PixNerd-XL/16.
+*   **Competitive Text-to-Image Performance**: Extends to text-to-image applications, achieving a competitive 0.73 overall score on the GenEval benchmark and 80.9 overall score on the DPG benchmark with PixNerd-XXL/16.
+*   **Efficient Neural Field Representation**: Leverages efficient neural field representations for optimized performance.
+
+## Visualizations
+![](https://huggingface.co/MCG-NJU/PixNerd-XL-P16-C2I/resolve/main/figs/pixelnerd_teaser.png)
+![](https://huggingface.co/MCG-NJU/PixNerd-XL-P16-C2I/resolve/main/figs/pixnerd_multires.png)
+
+## Revision of the inference time statistics
+Deeply sorry for this mistake, the single-step inference time of SiT-L/2 and Baseline-L is missing a zero (0.097s vs 0.0097s). The single-step inference time of PixNerd and Baseline is close.
+![image.png](https://cdn-uploads.huggingface.co/production/uploads/66615c855fd9d736e670e0a9/vEGp4Lthv9JDjDa8Gvyze.png)
+
+## Checkpoints
+
+| 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
+![](https://huggingface.co/MCG-NJU/PixNerd-XL-P16-C2I/resolve/main/figs/demo.png)
+We provide online demos for PixNerd-XXL/16 (text-to-image) on HuggingFace Spaces.
+
+HF spaces: [https://huggingface.co/spaces/MCG-NJU/PixNerd](https://huggingface.co/spaces/MCG-NJU/PixNerd)
+
+To host the local gradio demo, run the following command:
+```bash
+# for text-to-image applications
+python app.py --config configs_t2i/inference_heavydecoder.yaml  --ckpt_path=XXX.ckpt
+```
+
+## Usage
+For C2i (ImageNet), we use ADM evaluation suite to report FID.
+
+First, install the necessary dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+To run inference:
+```bash
+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 training:
+```bash
+python main.py fit -c configs_c2i/pix256std1_repa_pixnerd_xl.yaml
+```
+For T2i, we use GenEval and DPG to collect metrics.
+
+## Reference
+```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).