# Conditional LoRA Parameter Generation

arXiv: https://arxiv.org/abs/2408.01415 · HTML: https://mlsanity.com/paper/2408.01415/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 58 references that are in the map.
- First posted on arXiv 2 Aug 2024.

## Builds on (most cited first, 30 of 44)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (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)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [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)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://mlsanity.com/paper/2203.05482/) (2022)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)

## Built on by (most cited first, 16 of 16)

- [Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights](https://mlsanity.com/paper/2506.16406/) (2025)
- [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)
- [Structure Is Not Enough: Leveraging Behavior for Neural Network Weight Reconstruction](https://mlsanity.com/paper/2503.17138/) (2025)
- [Slot-ID: Identity-Preserving Video Generation from Reference Videos via Slot-Based Temporal Identity Encoding](https://mlsanity.com/paper/2601.01352/) (2026)
- [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)
- [HyperFlow: Gradient-Free Emulation of Few-Shot Fine-Tuning](https://mlsanity.com/paper/2504.15323/) (2025)
- [On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs](https://mlsanity.com/paper/2509.25214/) (2025)
- [NeuroGen: Neural Network Parameter Generation via Large Language Models](https://mlsanity.com/paper/2505.12470/) (2025)
- [Semantic-guided LoRA Parameters Generation](https://mlsanity.com/paper/2509.10535/) (2025)
- [Robotic Policy Adaptation via Weight-Space Meta-Learning](https://mlsanity.com/paper/2606.07217/) (2026)
- [PhaseLoRA: Control-Regime-Conditioned Low-Rank Adaptation for Continuous-Action Vision-Language-Action Policies](https://mlsanity.com/paper/2608.15285/) (2026)
- [Good Agentic Friends Do Not Just Give Verbal Advice: They Can Update Your Weights](https://mlsanity.com/paper/2605.13839/) (2026)
- [ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services](https://mlsanity.com/paper/2606.02606/) (2026)
- [Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork](https://mlsanity.com/paper/2606.26772/) (2026)
- [WeightCLIP: Aligning Datasets and Models for Weight Space Learning](https://mlsanity.com/paper/2607.03551/) (2026)

## Abstract (arXiv)

Generative models have achieved remarkable success in image, video, and text domains. Inspired by this, researchers have explored utilizing generative models to generate neural network parameters. However, these efforts have been limited by the parameter size and the practicality of generating high-performance parameters. In this paper, we propose COND P-DIFF, a novel approach that demonstrates the feasibility of controllable high-performance parameter generation, particularly for LoRA (Low-Rank Adaptation) weights, during the fine-tuning process. Specifically, we employ an autoencoder to extract efficient latent representations for parameters. We then train a conditional latent diffusion model to synthesize high-performing model parameters from random noise based on specific task conditions. Experimental results in both computer vision and natural language processing domains consistently demonstrate that COND P-DIFF can generate high-performance parameters conditioned on the given task. Moreover, we observe that the parameter distribution generated by COND P-DIFF exhibits differences compared to the distribution obtained through normal optimization methods, indicating a certain level of generalization capability. Our work paves the way for further exploration of condition-driven parameter generation, offering a promising direction for task-specific adaptation of neural networks.