GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing

• URL: http://arxiv.org/abs/2407.05600v2
• Date: Mon, 28 Oct 2024 14:08:13 GMT
• Title: GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing
• Authors: Zhenyu Wang, Aoxue Li, Zhenguo Li, Xihui Liu,
• Abstract summary: GenArtist is a unified image generation and editing system coordinated by a multimodal large language model (MLLM) agent. We integrate a comprehensive range of existing models into the tool library and utilize the agent for tool selection and execution. Experiments demonstrate that GenArtist can perform various generation and editing tasks, achieving state-of-the-art performance.
• Score: 60.09562648953926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the success achieved by existing image generation and editing methods, current models still struggle with complex problems including intricate text prompts, and the absence of verification and self-correction mechanisms makes the generated images unreliable. Meanwhile, a single model tends to specialize in particular tasks and possess the corresponding capabilities, making it inadequate for fulfilling all user requirements. We propose GenArtist, a unified image generation and editing system, coordinated by a multimodal large language model (MLLM) agent. We integrate a comprehensive range of existing models into the tool library and utilize the agent for tool selection and execution. For a complex problem, the MLLM agent decomposes it into simpler sub-problems and constructs a tree structure to systematically plan the procedure of generation, editing, and self-correction with step-by-step verification. By automatically generating missing position-related inputs and incorporating position information, the appropriate tool can be effectively employed to address each sub-problem. Experiments demonstrate that GenArtist can perform various generation and editing tasks, achieving state-of-the-art performance and surpassing existing models such as SDXL and DALL-E 3, as can be seen in Fig. 1. Project page is https://zhenyuw16.github.io/GenArtist_page.

Related papers

• EditAR: Unified Conditional Generation with Autoregressive Models [58.093860528672735]
  We propose EditAR, a single unified autoregressive framework for a variety of conditional image generation tasks. The model takes both images and instructions as inputs, and predicts the edited images tokens in a vanilla next-token paradigm. We evaluate its effectiveness across diverse tasks on established benchmarks, showing competitive performance to various state-of-the-art task-specific methods.
  arXiv  Detail & Related papers  (2025-01-08T18:59:35Z)
• UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generation [64.8341372591993]
  We propose a new approach to unify controllable generation within a single framework. Specifically, we propose the unified image-instruction adapter (UNIC-Adapter) built on the Multi-Modal-Diffusion Transformer architecture. Our UNIC-Adapter effectively extracts multi-modal instruction information by incorporating both conditional images and task instructions.
  arXiv  Detail & Related papers  (2024-12-25T15:19:02Z)
• BrushEdit: All-In-One Image Inpainting and Editing [79.55816192146762]
  BrushEdit is a novel inpainting-based instruction-guided image editing paradigm. We devise a system enabling free-form instruction editing by integrating MLLMs and a dual-branch image inpainting model. Our framework effectively combines MLLMs and inpainting models, achieving superior performance across seven metrics.
  arXiv  Detail & Related papers  (2024-12-13T17:58:06Z)
• GraPE: A Generate-Plan-Edit Framework for Compositional T2I Synthesis [10.47359822447001]
  We present an alternate paradigm for T2I synthesis, decomposing the task of complex multi-step generation into three steps. Our approach derives its strength from the fact that it is modular in nature, is training free, and can be applied over any combination of image generation and editing models.
  arXiv  Detail & Related papers  (2024-12-08T22:29:56Z)
• GenMAC: Compositional Text-to-Video Generation with Multi-Agent Collaboration [20.988801611785522]
  We propose GenMAC, an iterative, multi-agent framework that enables compositional text-to-video generation. The collaborative workflow includes three stages: Design, Generation, and Redesign. To tackle diverse scenarios of compositional text-to-video generation, we design a self-routing mechanism to adaptively select the proper correction agent from a collection of correction agents each specialized for one scenario.
  arXiv  Detail & Related papers  (2024-12-05T18:56:05Z)
• SPAgent: Adaptive Task Decomposition and Model Selection for General Video Generation and Editing [50.098005973600024]
  We propose a novel video generation and editing system powered by our Semantic Planning Agent (SPAgent) SPAgent bridges the gap between diverse user intents and the effective utilization of existing generative models. Experimental results demonstrate that the SPAgent effectively coordinates models to generate or edit videos.
  arXiv  Detail & Related papers  (2024-11-28T08:07:32Z)
• Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation [72.6168579583414]
  CompAgent is a training-free approach for compositional text-to-image generation with a large language model (LLM) agent as its core. Our approach achieves more than 10% improvement on T2I-CompBench, a comprehensive benchmark for open-world compositional T2I generation.
  arXiv  Detail & Related papers  (2024-01-28T16:18:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.