# Neural Network Diffusion

arXiv: https://arxiv.org/abs/2402.13144 · HTML: https://mlsanity.com/paper/2402.13144/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 51 of its 81 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 30 of 51)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)

## Built on by (most cited first, 30 of 42)

- [Symmetry in Neural Network Parameter Spaces](https://mlsanity.com/paper/2506.13018/) (2025)
- [Noise Hypernetworks: Amortizing Test-Time Compute in Diffusion Models](https://mlsanity.com/paper/2508.09968/) (2025)
- [A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training](https://mlsanity.com/paper/2405.17403/) (2024)
- [Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights](https://mlsanity.com/paper/2506.16406/) (2025)
- [Diffusion Models are Evolutionary Algorithms](https://mlsanity.com/paper/2410.02543/) (2024)
- [Conditional LoRA Parameter Generation](https://mlsanity.com/paper/2408.01415/) (2024)
- [Recurrent Diffusion for Large-Scale Parameter Generation](https://mlsanity.com/paper/2501.11587/) (2025)
- [In-Context Meta LoRA Generation](https://mlsanity.com/paper/2501.17635/) (2025)
- [Generative Modeling of Weights: Generalization or Memorization?](https://mlsanity.com/paper/2506.07998/) (2025)
- [Unsupervised Model Tree Heritage Recovery](https://mlsanity.com/paper/2405.18432/) (2024)
- [Text-to-Model: Text-Conditioned Neural Network Diffusion for Train-Once-for-All Personalization](https://mlsanity.com/paper/2405.14132/) (2024)
- [ElastoGen: 4D Generative Elastodynamics](https://mlsanity.com/paper/2405.15056/) (2024)
- [PFDiff: Training-Free Acceleration of Diffusion Models Combining Past and Future Scores](https://mlsanity.com/paper/2408.08822/) (2024)
- [Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models](https://mlsanity.com/paper/2409.17836/) (2024)
- [DeepWeightFlow: Re-Basined Flow Matching for Generating Neural Network Weights](https://mlsanity.com/paper/2601.05052/) (2026)
- [WARPD: World model Assisted Reactive Policy Diffusion](https://mlsanity.com/paper/2410.14040/) (2024)
- [HY-WU (Part I): An Extensible Functional Neural Memory Framework and An Instantiation in Text-Guided Image Editing](https://mlsanity.com/paper/2603.07236/) (2026)
- [Few-shot Implicit Function Generation via Equivariance](https://mlsanity.com/paper/2501.01601/) (2025)
- [On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs](https://mlsanity.com/paper/2509.25214/) (2025)
- [PILD: Physics-Informed Learning via Diffusion](https://mlsanity.com/paper/2601.21284/) (2026)
- [Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained Transformers](https://mlsanity.com/paper/2606.23607/) (2026)
- [Make Optimization Once and for All with Fine-grained Guidance](https://mlsanity.com/paper/2503.11462/) (2025)
- [NeuroGen: Neural Network Parameter Generation via Large Language Models](https://mlsanity.com/paper/2505.12470/) (2025)
- [DiffPINN: Generative diffusion-initialized physics-informed neural networks for accelerating seismic wavefield representation](https://mlsanity.com/paper/2506.00471/) (2025)
- [Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios](https://mlsanity.com/paper/2506.24063/) (2025)
- [Weight Space Representation Learning via Neural Field Adaptation](https://mlsanity.com/paper/2512.01759/) (2025)
- [Puppet-CNN: Continuous Parameter Dynamics for Input-Adaptive Convolutional Networks](https://mlsanity.com/paper/2411.12876/) (2024)
- [Model Diffusion for Certifiable Few-shot Transfer Learning](https://mlsanity.com/paper/2502.06970/) (2025)
- [Fast Kernel-Space Diffusion for Remote Sensing Pansharpening](https://mlsanity.com/paper/2505.18991/) (2025)
- [Reimagining Parameter Space Exploration with Diffusion Models](https://mlsanity.com/paper/2506.17807/) (2025)

## Abstract (arXiv)

Diffusion models have achieved remarkable success in image and video generation. In this work, we demonstrate that diffusion models can also textitgenerate high-performing neural network parameters. Our approach is simple, utilizing an autoencoder and a diffusion model. The autoencoder extracts latent representations of a subset of the trained neural network parameters. Next, a diffusion model is trained to synthesize these latent representations from random noise. This model then generates new representations, which are passed through the autoencoder's decoder to produce new subsets of high-performing network parameters. Across various architectures and datasets, our approach consistently generates models with comparable or improved performance over trained networks, with minimal additional cost. Notably, we empirically find that the generated models are not memorizing the trained ones. Our results encourage more exploration into the versatile use of diffusion models. Our code is available hrefhttps://github.com/NUS-HPC-AI-Lab/Neural-Network-Diffusionhere.