Create README.md

README.md

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+---
+language:
+- en
+license: mit
+tags:
+- image-generation
+- autoregressive
+- next-scale-prediction
+- exposure-bias
+- post-training
+- pytorch
+- imagenet
+library_name: pytorch
+inference: false
+model-index:
+- name: ZGZzz/SAR
+  results:
+  - task:
+      type: image-generation
+      name: Image Generation
+    dataset:
+      name: ImageNet 256×256
+      type: imagenet-1k
+      config: 256x256
+      split: validation
+    metrics:
+    - type: fid
+      name: FID (FlexVAR-d16, +SAR)
+      value: 2.89
+      higher_is_better: false
+    - type: fid
+      name: FID (FlexVAR-d20, +SAR)
+      value: 2.35
+      higher_is_better: false
+    - type: fid
+      name: FID (FlexVAR-d24, +SAR)
+      value: 2.14
+      higher_is_better: false
+datasets:
+- ILSVRC/imagenet-1k
+base_model:
+- jiaosiyu1999/FlexVAR
+pipeline_tag: text-to-image
+---
+
+<div align="center">
+<h1>Rethinking Training Dynamics in Scale-wise Autoregressive Generation</h1>
+
+<a href="https://gengzezhou.github.io/" target="_blank">Gengze Zhou</a><sup>1*</sup>,
+<a href="https://chongjiange.github.io/" target="_blank">Chongjian Ge</a><sup>2</sup>,
+<a href="https://www.cs.unc.edu/~airsplay/" target="_blank">Hao Tan</a><sup>2</sup>,
+<a href="https://pages.cs.wisc.edu/~fliu/" target="_blank">Feng Liu</a><sup>2</sup>,
+<a href="https://yiconghong.me" target="_blank">Yicong Hong</a><sup>2</sup>
+
+<sup>1</sup>Australian Institute for Machine Learning, Adelaide University &nbsp;&nbsp;&nbsp;
+<sup>2</sup>Adobe Research
+
+[![arXiv](https://img.shields.io/badge/arXiv-2512.06421-b31b1b.svg)](https://arxiv.org/abs/2512.06421)&nbsp;
+[![huggingface weights](https://img.shields.io/badge/%F0%9F%A4%97%20Weights-SAR--ckpts-yellow)](https://huggingface.co/ZGZzz/SAR)&nbsp;
+[![project page](https://img.shields.io/badge/Project%20Page-SAR-blue)](https://gengzezhou.github.io/SAR)&nbsp;
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+
+</div>
+
+## Model Description
+
+**Self-Autoregressive Refinement (SAR)** is a lightweight *post-training* algorithm for **scale-wise autoregressive (AR)** image generation (next-scale prediction). SAR mitigates **exposure bias** by addressing (1) train–test mismatch (teacher forcing vs. student forcing) and (2) imbalance in scale-wise learning difficulty.
+
+SAR consists of:
+- **Stagger-Scale Rollout (SSR):** a two-step rollout (teacher-forcing → student-forcing) with minimal compute overhead (one extra forward pass).
+- **Contrastive Student-Forcing Loss (CSFL):** stabilizes student-forced training by aligning predictions with a teacher trajectory under self-generated contexts.
+
+## Key Features
+
+- **Minimal overhead:** SSR adds only a lightweight additional forward pass to train on self-generated content.
+- **General post-training recipe:** applies on top of pretrained scale-wise AR models.
+- **Empirical gains:** e.g., reported **5.2% FID reduction** on FlexVAR-d16 with 10 SAR epochs.
+
+## Model Zoo (ImageNet 256×256)
+
+| Model | Params | Base FID ↓ | SAR FID ↓ | SAR Weights |
+|---|---:|---:|---:|---|
+| SAR-d16 | 310M | 3.05 | **2.89** | `pretrained/SARd16-epo179.pth` |
+| SAR-d20 | 600M | 2.41 | **2.35** | `pretrained/SARd20-epo249.pth` |
+| SAR-d24 | 1.0B | 2.21 | **2.14** | `pretrained/SARd24-epo349.pth` |
+
+## How to Use
+
+### Installation
+
+```bash
+git clone https://github.com/GengzeZhou/SAR.git
+conda create -n sar python=3.10 -y
+conda activate sar
+pip install -r requirements.txt
+# optional
+pip install flash-attn xformers
+```
+
+### Sampling / Inference (Example)
+
+```python
+import torch
+from models import build_vae_var
+from torchvision.utils import save_image
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Build VAE + VAR backbone (example: depth=16)
+vae, model = build_vae_var(
+    V=8912, Cvae=32, device=device,
+    num_classes=1000, depth=16,
+    vae_ckpt="pretrained/FlexVAE.pth",
+)
+
+# Load SAR checkpoint
+ckpt = torch.load("pretrained/SARd16-epo179.pth", map_location="cpu")
+if "trainer" in ckpt:
+    ckpt = ckpt["trainer"]["var_wo_ddp"]
+model.load_state_dict(ckpt, strict=False)
+model.eval()
+
+with torch.no_grad():
+    labels = torch.tensor([207, 88, 360, 387], device=device)  # example ImageNet classes
+    images = model.autoregressive_infer_cfg(
+        vqvae=vae,
+        B=4,
+        label_B=labels,
+        cfg=2.5,
+        top_k=900,
+        top_p=0.95,
+    )
+
+save_image(images, "samples.png", normalize=True, value_range=(-1, 1), nrow=4)
+```
+
+## Training (SAR Post-Training)
+
+```bash
+bash scripts/train_SAR_d16.sh
+bash scripts/train_SAR_d20.sh
+bash scripts/train_SAR_d24.sh
+```
+
+## Evaluation
+
+```bash
+bash scripts/setup_eval.sh
+bash scripts/eval_SAR_d16.sh
+bash scripts/eval_SAR_d20.sh
+bash scripts/eval_SAR_d24.sh
+```
+
+## Acknowledgements
+
+This codebase builds upon **VAR** and **FlexVAR**.
+
+## Citation
+
+```bibtex
+@article{zhou2025rethinking,
+  title={Rethinking Training Dynamics in Scale-wise Autoregressive Generation},
+  author={Zhou, Gengze and Ge, Chongjian and Tan, Hao and Liu, Feng and Hong, Yicong},
+  journal={arXiv preprint arXiv:2512.06421},
+  year={2025}
+}
+```