Add model card, link to paper and GitHub repository

README.md

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+---
+pipeline_tag: unconditional-image-generation
+---
+
+# Parallel Rollout Approximation (PRA)
+
+This repository contains the weights for **Parallel Rollout Approximation (PRA)**, a scalable framework for class-conditional pixel-space autoregressive image generation.
+
+More details can be found in the paper [Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation](https://huggingface.co/papers/2606.27978).
+
+* **Repository:** [GitHub Repository](https://github.com/MangataX/PRA)
+* **Paper:** [arXiv:2606.27978](https://huggingface.co/papers/2606.27978)
+
+## Model Description
+
+Parallel Rollout Approximation (PRA) is a pixel-space continuous-token autoregressive (AR) generation model. PRA generates low-dimensional intermediate states instead of high-dimensional pixel patches, mapping them back to pixel-space tokens with a pixel decoder. It effectively mitigates error accumulation during autoregressive steps by approximating the pixel-feedback interface encountered during inference-time rollout while retaining parallel teacher-forced training.
+
+## Model Checkpoints
+
+The following checkpoints are available:
+
+| Model | Params | FID (256x256) | Weight |
+|:---:|:---:|:---:|:---:|
+| PRA-S | 135M | 2.58 | [PRA_S.pt](https://huggingface.co/MangataX/PRA/blob/main/PRA_S.pt) |
+| PRA-B | 250M | 2.21 | [PRA_B.pt](https://huggingface.co/MangataX/PRA/blob/main/PRA_B.pt) |
+| PRA-L | 511M | 1.94 | [PRA_L.pt](https://huggingface.co/MangataX/PRA/blob/main/PRA_L.pt) |
+
+## Environment & Usage
+
+For environment setup, training, and evaluation scripts, please refer to the official [GitHub Repository](https://github.com/MangataX/PRA).
+
+### Sampling Example
+
+You can run distributed class-balanced sampling using the `sample_ddp.py` script provided in the repository:
+
+```shell
+ckpt=your_ckpt_path
+sample_dir=your_result_path
+
+torchrun --nnodes=1 --nproc_per_node=4 --node_rank=0 \
+  sample_ddp.py \
+  --ckpt $ckpt \
+  --sample-dir $sample_dir \
+  --model PRA-L \
+  --image-size 256 \
+  --patch-size 16 \
+  --latent-dim 16 \
+  --cfg-scale 4.1 \
+  --sample-steps 100 \
+  --sampler euler_maruyama \
+  --per-proc-batch-size 200 \
+  --sample-mask-rate 0.9 \
+  --token-mask-rate 0.5 \
+  --save-png
+```
+
+## Citation
+
+```bibtex
+@article{xu2026parallel,
+  title={Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation},
+  author={Xu, Jiayi and He, Di and Ke, Guolin},
+  journal={arXiv preprint arXiv:2606.27978},
+  year={2026}
+}
+```