Scaling Up Parameter Generation: A Recurrent Diffusion Approach
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Abstract
Parameter generation has long struggled to match the scale of today's large vision and language models, curbing its broader utility. In this paper, we introduce Recurrent Diffusion for Large-Scale Parameter Generation (RPG), a novel framework that generates full neural network parameters—up to hundreds of millions—on a single GPU. Our approach first partitions a network's parameters into non-overlapping 'tokens', each corresponding to a distinct portion of the model. A recurrent mechanism then learns the inter-token relationships, producing 'prototypes' which serve as conditions for a diffusion process that ultimately synthesizes the parameters. Across a spectrum of architectures and tasks—including ResNets, ConvNeXts and ViTs on ImageNet-1K and COCO, and even LoRA-based LLMs—RPG achieves performance on par with fully trained networks while avoiding excessive memory overhead. Notably, it generalizes beyond its training set to generate valid parameters for previously unseen tasks, highlighting its flexibility in open-ended scenarios. By overcoming the longstanding memory and scalability barriers, RPG serves as a critical advance in 'AI generating AI', potentially enabling efficient weight generation at scales previously deemed infeasible.
Environment
Before you get started, you need to set up a conda environment first.
- Create your conda environment.
conda create -n rpg python=3.11
conda activate rpg
conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia
- Install mamba-ssm. (You may run into compilation issues, refer to the official mamba-ssm repository for details.)
pip install causal-conv1d
pip install mamba-ssm[causal-conv1d]
- Install other dependencies for this repository.
git lfs install
git clone https://huggingface.co/MTDoven/Recurrent-Parameter-Generation.git
cd Recurrent-Parameter-Generation
pip install -r requirements.txt
Quick Start
Try to generate with RPG model.
cd ./workspace
CUDA_VISIBLE_DEVICES=0 sh demo.sh
# CUDA_VISIBLE_DEVICES=<GPU_index> sh demo.sh
description: "Give me a model to select all living things"
expected_class: [0,0,1,1,1,1,1,1,0,0] # bird, cat, deer, dog, frog, horse
description: "Find all vehicles that operate on roads"
expected_class: [0,1,0,0,0,0,0,0,0,1] # automobile, truck
description: "Select all things that can fly"
expected_class: [1,0,1,0,0,0,0,0,0,0] # airplane, bird
description: "Find all transportation methods that travel on water"
expected_class: [0,0,0,0,0,0,0,0,1,0] # ship
description: "Classify all mammals"
expected_class: [0,0,0,1,1,1,0,1,0,0] # cat, deer, dog, horse
description: "Find all animals with fur"
expected_class: [0,0,1,1,1,1,0,1,0,0] # bird, cat, deer, dog, horse
description: "Select all pets commonly found in households"
expected_class: [0,0,1,1,0,1,0,0,0,0] # bird, cat, dog
description: "Identify all cold-blooded animals"
expected_class: [0,0,0,0,0,0,1,0,0,0] # frog
description: "Find all objects that can carry cargo"
expected_class: [1,1,0,0,0,0,0,0,1,1] # airplane, automobile, ship, truck
description: "Select all things used for commercial transportation"
expected_class: [1,1,0,0,0,0,0,0,1,1] # airplane, automobile, ship, truck
description: "Identify all animals that can swim naturally"
expected_class: [0,0,0,1,0,0,1,0,0,0] # cat, frog
description: "Find all things with wheels"
expected_class: [1,1,0,0,0,0,0,0,0,1] # airplane, automobile, truck
description: "Select all creatures with four legs"
expected_class: [0,0,0,1,1,1,0,1,0,0] # cat, deer, dog, horse
description: "Identify all creatures that live in forests"
expected_class: [0,0,1,1,1,1,0,0,0,0] # bird, cat, deer, dog
description: "Find all animals that can live near water"
expected_class: [0,0,1,0,0,0,1,0,0,0] # bird, frog
description: "Select all man-made objects"
expected_class: [1,1,0,0,0,0,0,0,1,1] # airplane, automobile, ship, truck
description: "Find all things that make noise naturally"
expected_class: [0,0,1,1,1,1,1,1,0,0] # all animals
description: "Identify all animals that can climb trees"
expected_class: [0,0,1,1,0,1,0,0,0,0] # bird, cat, dog
"Select all animals that hunt other animals"
expected_class: [0,0,0,1,0,1,0,0,0,0] # cat, dog
description: "Find all things that are both man-made and can operate on water"
expected_class: [0,0,0,0,0,0,0,0,1,0] # ship
description: "Select all animals that are both pets and can climb"
expected_class: [0,0,0,1,0,1,0,0,0,0] # cat, dog
You can get more information from Github and Project-Page.
Acknowledgment
We thank Zhiyuan Liang, Zhuang Liu, Gongfan Fang, Xuanlei Zhao, Yuhao Zhou, Mingjia Shi, Zangwei Zheng, Ziheng Qin, and Tianlong Chen for valuable discussions and feedbacks. This research is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-PhD-2021-08-008).
Citation
@misc{wang2025recurrent,
title={Scaling Up Parameter Generation: A Recurrent Diffusion Approach},
author={Wang, Kai and Tang, Dongwen and Zhao, Wangbo and Schürholt, Konstantin and Wang, Zhangyang and You, Yang},
year={2025},
}