DiffGraph: An Automated Agent-driven Model Merging Framework for In-the-Wild Text-to-Image Generation
The rapid growth of the text-to-image (T2I) community has fostered a thriving online ecosystem of expert models, which are variants of pretrained diffusion models specialized for diverse generative abilities. Yet, existing model merging methods remain limited in fully leveraging abundant online expert resources and still struggle to meet diverse in-the-wild user needs. We present DiffGraph, a novel agent-driven graph-based model merging framework, which automatically harnesses online experts and flexibly merges them for diverse user needs. Our DiffGraph constructs a scalable graph and organizes ever-expanding online experts within it through node registration and calibration. Then, DiffGraph dynamically activates specific subgraphs based on user needs, enabling flexible combinations of different experts to achieve user-desired generation. Extensive experiments show the efficacy of our method.
Code (0)
등록된 구현이 없습니다.
Tasks
Text-to-Image GenerationSimilar Papers 제목 키워드 기반
DiffGraph: Heterogeneous Graph Diffusion Model
Recent advances in Graph Neural Networks (GNNs) have revolutionized graph-structured data modeling, yet traditional GNNs struggle with complex heterogeneous structures prevalent in real-world scenarios. Despite progress …
DenoisingGraph GenerationLink Predictionmodel+1Interactive Training: Feedback-Driven Neural Network Optimization
Traditional neural network training typically follows fixed, predefined optimization recipes, lacking the flexibility to dynamically respond to instabilities or emerging training issues. In this paper, we introduce Inter…
Adversarial Stress Testing of Role-Playing Language Agents using Multi-Agent Evaluation
Role-Playing Language Agents (RPLAs) are increasingly deployed in high-stakes applications such as healthcare assistance, customer support, and education, where maintaining consistent personas, ethical constraints, and b…
LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents
Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks. To further tailor LLMs to specific domains or applications, post-training techniques such as Supervised Fine-Tuning (SF…
Instruction FollowingA Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways
The steady increase in the number of vehicles operating on the highways continues to exacerbate congestion, accidents, energy consumption, and greenhouse gas emissions. Emerging mobility systems, e.g., connected and auto…
Deep Reinforcement LearningReinforcement Learning (RL)