title: ComfyMind: Toward General-Purpose Generation via Tree-Based Planning and
    Reactive Feedback
summary: With the rapid advancement of generative models, general-purpose generation
  has gained increasing attention as a promising approach to unify diverse tasks
  across modalities within a single system. Despite this progress, existing
  open-source frameworks often remain fragile and struggle to support complex
  real-world applications due to the lack of structured workflow planning and
  execution-level feedback. To address these limitations, we present ComfyMind, a
  collaborative AI system designed to enable robust and scalable general-purpose
  generation, built on the ComfyUI platform. ComfyMind introduces two core
  innovations: Semantic Workflow Interface (SWI) that abstracts low-level node
  graphs into callable functional modules described in natural language, enabling
  high-level composition and reducing structural errors; Search Tree Planning
  mechanism with localized feedback execution, which models generation as a
  hierarchical decision process and allows adaptive correction at each stage.
  Together, these components improve the stability and flexibility of complex
  generative workflows. We evaluate ComfyMind on three public benchmarks:
  ComfyBench, GenEval, and Reason-Edit, which span generation, editing, and
  reasoning tasks. Results show that ComfyMind consistently outperforms existing
  open-source baselines and achieves performance comparable to GPT-Image-1.
  ComfyMind paves a promising path for the development of open-source
  general-purpose generative AI systems. Project page:
  https://github.com/LitaoGuo/ComfyMind
paper_authors._id: 6836ab69f5ad887c90517254
paper_authors.name: Litao Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:43:50.189Z
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paper_authors.avatarUrl: /avatars/b5ca2039731fec9d8e95d6d15b7719f5.svg
paper_authors.fullname: litao Guo
paper_authors.type: user
paper_authors.user: Riversideli
paper_authors._id: 6836ab69f5ad887c90517255
paper_authors.name: Xinli Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:43:41.201Z
paper_authors._id: 64b4ab62eec33e27dcd733b5
paper_authors.avatarUrl: /avatars/0a9bf220c9a5efe7279f9b287b087d36.svg
paper_authors.fullname: Xinli XU
paper_authors.type: user
paper_authors.user: Xxlbigbrother
paper_authors._id: 6836ab69f5ad887c90517256
paper_authors.name: Luozhou Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:43:35.119Z
paper_authors._id: 6340333a733f9eef46913dc8
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paper_authors.fullname: luozhou wang
paper_authors.type: user
paper_authors.user: wileewang
paper_authors._id: 6836ab69f5ad887c90517257
paper_authors.name: Jiantao Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:43:28.714Z
paper_authors._id: 6332e2689bf698ce68a22e8c
paper_authors.avatarUrl: /avatars/c1922acfda2e6d2fe7b03194a404eb10.svg
paper_authors.fullname: JIANTAO LIN
paper_authors.type: user
paper_authors.user: LTT
paper_authors._id: 6836ab69f5ad887c90517258
paper_authors.name: Jinsong Zhou
paper_authors._id: 6836ab69f5ad887c90517259
paper_authors.name: Zixin Zhang
paper_authors._id: 6836ab69f5ad887c9051725a
paper_authors.name: Bolan Su
paper_authors._id: 6836ab69f5ad887c9051725b
paper_authors.name: Ying-Cong Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:42:54.177Z
paper_authors._id: 655cba1d87b67834000590e8
paper_authors.avatarUrl: /avatars/3bd43b7c9351f65b8f38f4c8237a0146.svg
paper_authors.fullname: Yingcong Chen
paper_authors.type: user
paper_authors.user: yingcongchen
paper_title: ComfyMind: Toward General-Purpose Generation via Tree-Based Planning and
    Reactive Feedback
paper_summary: With the rapid advancement of generative models, general-purpose generation
  has gained increasing attention as a promising approach to unify diverse tasks
  across modalities within a single system. Despite this progress, existing
  open-source frameworks often remain fragile and struggle to support complex
  real-world applications due to the lack of structured workflow planning and
  execution-level feedback. To address these limitations, we present ComfyMind, a
  collaborative AI system designed to enable robust and scalable general-purpose
  generation, built on the ComfyUI platform. ComfyMind introduces two core
  innovations: Semantic Workflow Interface (SWI) that abstracts low-level node
  graphs into callable functional modules described in natural language, enabling
  high-level composition and reducing structural errors; Search Tree Planning
  mechanism with localized feedback execution, which models generation as a
  hierarchical decision process and allows adaptive correction at each stage.
  Together, these components improve the stability and flexibility of complex
  generative workflows. We evaluate ComfyMind on three public benchmarks:
  ComfyBench, GenEval, and Reason-Edit, which span generation, editing, and
  reasoning tasks. Results show that ComfyMind consistently outperforms existing
  open-source baselines and achieves performance comparable to GPT-Image-1.
  ComfyMind paves a promising path for the development of open-source
  general-purpose generative AI systems. Project page:
  https://github.com/LitaoGuo/ComfyMind
paper_ai_summary: ComfyMind, a collaborative AI system built on ComfyUI, enhances generative workflows with a Semantic Workflow Interface and Search Tree Planning mechanism, outperforming existing open-source systems across generation, editing, and reasoning tasks.
paper_ai_keywords: Semantic Workflow Interface Search Tree Planning mechanism generative models general-purpose generation generative workflows