Update model card for Neon: Negative Extrapolation From Self-Training

This PR significantly enhances the model card for `Neon: Negative Extrapolation From Self-Training`, a method for improving generative AI models.

It adds:
- The `pipeline_tag: unconditional-image-generation` to ensure proper discoverability on the Hugging Face Hub.
- A comprehensive description of the model, including its abstract, key methodology, and performance benchmarks.
- Links to the official paper: [Neon: Negative Extrapolation From Self-Training Improves Image Generation](https://huggingface.co/papers/2510.03597).
- A link to the official GitHub repository: [https://github.com/SinaAlemohammad/Neon](https://github.com/SinaAlemohammad/Neon).
- Details on the method, benchmark performance, repository map, citation, contact information, and acknowledgments, all extracted from the official GitHub README.
- A reference to the GitHub repository for detailed quickstart and evaluation instructions, adhering to the "no made-up code" policy for sample usage.

The `license` remains `mit` as found in the existing model card, and `library_name` is intentionally omitted due to the lack of direct Hugging Face library compatibility for automated inference snippets.

README.md

@@ -1,3 +1,79 @@
----
-license: mit
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+---
+license: mit
+pipeline_tag: unconditional-image-generation
+---
+
+# Neon: Negative Extrapolation From Self-Training Improves Image Generation
+
+This repository contains the official implementation for the paper [Neon: Negative Extrapolation From Self-Training Improves Image Generation](https://huggingface.co/papers/2510.03597).
+
+**Abstract**
+
+Scaling generative AI models is bottlenecked by the scarcity of high-quality training data. The ease of synthesizing from a generative model suggests using (unverified) synthetic data to augment a limited corpus of real data for the purpose of fine-tuning in the hope of improving performance. Unfortunately, however, the resulting positive feedback loop leads to model autophagy disorder (MAD, aka model collapse) that results in a rapid degradation in sample quality and/or diversity. In this paper, we introduce Neon (for Negative Extrapolation frOm self-traiNing), a new learning method that turns the degradation from self-training into a powerful signal for self-improvement. Given a base model, Neon first fine-tunes it on its own self-synthesized data but then, counterintuitively, reverses its gradient updates to extrapolate away from the degraded weights. We prove that Neon works because typical inference samplers that favor high-probability regions create a predictable anti-alignment between the synthetic and real data population gradients, which negative extrapolation corrects to better align the model with the true data distribution. Neon is remarkably easy to implement via a simple post-hoc merge that requires no new real data, works effectively with as few as 1k synthetic samples, and typically uses less than 1% additional training compute. We demonstrate Neon's universality across a range of architectures (diffusion, flow matching, autoregressive, and inductive moment matching models) and datasets (ImageNet, CIFAR-10, and FFHQ). In particular, on ImageNet 256x256, Neon elevates the xAR-L model to a new state-of-the-art FID of 1.02 with only 0.36% additional training compute.
+
+## Official Resources
+
+*   **Paper**: [https://huggingface.co/papers/2510.03597](https://huggingface.co/papers/2510.03597)
+*   **GitHub Repository**: [https://github.com/SinaAlemohammad/Neon](https://github.com/SinaAlemohammad/Neon)
+
+## Method
+
+![Algorithm 1: Neon — Negative Extrapolation from Self-Training](https://github.com/SinaAlemohammad/Neon/raw/main/assets/algorithm.png)
+
+**In one line:** sample with your usual inference to form a synthetic set $S$; briefly fine-tune the reference model on $S$ to get $\theta_s$; then **reverse** that update with a merge $\theta_{\text{neon}}=(1+w)\,\theta_r - w\,\theta_s$ (small $w>0$), which cancels mode-seeking drift and improves FID.
+
+## Benchmark Performance
+
+| Model type    | Dataset          | Base model FID | Neon FID (paper) | Download model                                                                                          |
+| ------------- | ---------------- | -------------: | ---------------: | ------------------------------------------------------------------------------------------------------- |
+| xAR-L         | ImageNet-256     |           1.28 |         **1.02** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_xARL_imagenet256.pth)          |
+| xAR-B         | ImageNet-256     |           1.72 |         **1.31** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_xARB_imagenet256.pth)          |
+| VAR d16       | ImageNet-256     |           3.30 |         **2.01** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_VARd16_imagenet256.pth)        |
+| VAR d36       | ImageNet-512     |           2.63 |         **1.70** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_VARd36_imagenet512.pth)        |
+| EDM (cond.)   | CIFAR-10 (32×32) |           1.78 |         **1.38** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_EDM_conditional_CIFAR10.pkl)   |
+| EDM (uncond.) | CIFAR-10 (32×32) |           1.98 |         **1.38** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_EDM_unconditional_CIFAR10.pkl) |
+| EDM           | FFHQ-64×64       |           2.39 |         **1.12** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_EDM_FFHQ.pkl)                  |
+| IMM           | ImageNet-256     |           1.99 |         **1.46** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_imm_imagenet256.pkl)           |
+
+## Quickstart & Evaluation
+
+For environment setup, downloading pretrained models, and evaluation scripts (for FID/IS), please refer to the [GitHub repository's Quickstart section](https://github.com/SinaAlemohammad/Neon#quickstart).
+
+## Repository Map
+
+```
+Neon/
+├── VAR/                # VAR baselines + eval scripts
+├── xAR/                # xAR baselines + eval scripts (uses MAR VAE)
+├── edm/                # EDM baselines + metrics/scripts
+├── imm/                # IMM baselines + eval scripts
+├── toy_appendix.ipynb  # 2D Gaussian toy example (diffusion & AR)
+├── download_models.sh  # Grab all checkpoints + FID refs
+├── environment.yml     # Reproducible env
+└── checkpoints/, fid_stats/ (created by the script)
+```
+
+## Citation
+
+```bibtex
+@article{neon2025,
+  title={Neon: Negative Extrapolation from Self-Training for Generative Models},
+  author={Alemohammad, Sina and collaborators},
+  journal={arXiv preprint},
+  year={2025}
+}
+```
+
+## Contact
+
+Questions? Reach out to **Sina Alemohammad** — [[email protected]](mailto:[email protected]).
+
+## Acknowledgments
+
+This repository builds upon and thanks the following projects:
+
+*   [VAR — Visual AutoRegressive Modeling](https://github.com/FoundationVision/VAR)
+*   [xAR — Beyond Next-Token: Next-X Prediction](https://github.com/OliverRensu/xAR)
+*   [IMM — Inductive Moment Matching](https://github.com/lumalabs/imm)
+*   [EDM — Elucidating the Design Space of Diffusion Models](https://github.com/NVlabs/edm)
+*   [MAR VAE (KL-16) tokenizer](https://huggingface.co/xwen99/mar-vae-kl16)