README.md

metadata

license: mit
pipeline_tag: unconditional-image-generation

Neon: Negative Extrapolation From Self-Training Improves Image Generation

Paper: Neon: Negative Extrapolation From Self-Training Improves Image Generation

Code: https://github.com/SinaAlemohammad/Neon

Introduction

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.

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Method

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.

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Benchmark Performance

Model type	Dataset	Base model FID	Neon FID (paper)	Download model
xAR-L	ImageNet-256	1.28	1.02	Download
xAR-B	ImageNet-256	1.72	1.31	Download
VAR d16	ImageNet-256	3.30	2.01	Download
VAR d36	ImageNet-512	2.63	1.70	Download
EDM (cond.)	CIFAR-10 (32×32)	1.78	1.38	Download
EDM (uncond.)	CIFAR-10 (32×32)	1.98	1.38	Download
EDM	FFHQ-64×64	2.39	1.12	Download
IMM	ImageNet-256	1.99	1.46	Download

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🚀 Quickstart

1) Environment

# from repo root
conda env create -f environment.yml
conda activate neon

2) Download pretrained models & FID stats

bash download_models.sh

This populates checkpoints/ and fid_stats/. Pretrained Neon models can also be downloaded from Hugging Face: https://huggingface.co/sinaalemohammad/Neon

3) Evaluate (FID/IS)

All examples assume 8 GPUs; adjust --nproc_per_node / batch sizes as needed.

xAR @ ImageNet‑256

# 1) VAE for xAR (credit: MAR)
hf download xwen99/mar-vae-kl16 --include kl16.ckpt --local-dir xAR/pretrained
# 2) Use it via:
#   --vae_path xAR/pretrained/kl16.ckpt

# xAR‑L
PYTHONPATH=xAR torchrun --standalone --nproc_per_node=8 xAR/calculate_fid.py \
  --model xar_large \
  --model_ckpt checkpoints/Neon_xARL_imagenet256.pth \
  --cfg 2.3 --vae_path xAR/pretrained/kl16.ckpt \
  --num_images 50000 --batch_size 64 --flow_steps 40 --img_size 256 \
  --fid_stats fid_stats/adm_in256_stats.npz

# xAR‑B
PYTHONPATH=xAR torchrun --standalone --nproc_per_node=8 xAR/calculate_fid.py \
  --model xar_base \
  --model_ckpt checkpoints/Neon_xARB_imagenet256.pth \
  --cfg 2.7 --vae_path xAR/pretrained/kl16.ckpt \
  --num_images 50000 --batch_size 32 --flow_steps 50 --img_size 256 \
  --fid_stats fid_stats/adm_in256_stats.npz

VAR @ ImageNet‑256 / 512

# d16 @ 256
PYTHONPATH=VAR/VAR_imagenet_256 torchrun --standalone --nproc_per_node=8 \
  VAR/VAR_imagenet_256/calculate_fid.py \
  --var_ckpt checkpoints/Neon_VARd16_imagenet256.pth \
  --num_images 50000 --batch_size 64 --img_size 256 \
  --fid_stats fid_stats/adm_in256_stats.npz

# d36 @ 512
PYTHONPATH=VAR/VAR_imagenet_512 torchrun --standalone --nproc_per_node=8 \
  VAR/VAR_imagenet_512/calculate_fid.py \
  --var_ckpt checkpoints/Neon_VARd36_imagenet512.pth \
  --num_images 50000 --batch_size 32 --img_size 512 \
  --fid_stats fid_stats/adm_in512_stats.npz

EDM (Karras et al.) @ CIFAR‑10 / FFHQ

# CIFAR‑10 (conditional)
PYTHONPATH=edm torchrun --standalone --nproc_per_node=8 edm/calculate_fid.py \
  --network_pkl checkpoints/Neon_EDM_conditional_CIFAR10.pkl \
  --ref https://nvlabs-fi-cdn.nvidia.com/edm/fid-refs/cifar10-32x32.npz \
  --seeds 0-49999 --max_batch_size 256 --num_steps 18

# CIFAR‑10 (unconditional)
PYTHONPATH=edm torchrun --standalone --nproc_per_node=8 edm/calculate_fid.py \
  --network_pkl checkpoints/Neon_EDM_unconditional_CIFAR10.pkl \
  --ref https://nvlabs-fi-cdn.nvidia.com/edm/fid-refs/cifar10-32x32.npz \
  --seeds 0-49999 --max_batch_size 256 --num_steps 18

# FFHQ‑64 (unconditional)
PYTHONPATH=edm torchrun --standalone --nproc_per_node=8 edm/calculate_fid.py \
  --network_pkl checkpoints/Neon_EDM_FFHQ.pkl \
  --ref https://nvlabs-fi-cdn.nvidia.com/edm/fid-refs/ffhq-64x64.npz \
  --seeds 0-49999 --max_batch_size 256 --num_steps 40

IMM @ ImageNet‑256

# IMM @ T = 8
PYTHONPATH=imm torchrun --standalone --nproc_per_node=8 imm/calculate_fid.py \
  --model_ckpt checkpoints/Neon_IMM_imagenet256.pth \
  --num_images 50000 --batch_size 64 --img_size 256 \
  --fid_stats fid_stats/adm_in256_stats.npz

---

📣 Citation

If you find Neon useful, please consider citing the paper:

@article{neon2025,
  title={Neon: Negative Extrapolation from Self-Training for Generative Models},
  author={Alemohammad, Sina and collaborators},
  journal={arXiv preprint},
  year={2025}
}