ComfyUI-R1: Exploring Reasoning Models for Workflow Generation

• URL: http://arxiv.org/abs/2506.09790v1
• Date: Wed, 11 Jun 2025 14:35:15 GMT
• Title: ComfyUI-R1: Exploring Reasoning Models for Workflow Generation
• Authors: Zhenran Xu, Yiyu Wang, Xue Yang, Longyue Wang, Weihua Luo, Kaifu Zhang, Baotian Hu, Min Zhang,
• Abstract summary: We introduce ComfyUI-R1, first large reasoning model for automated workflow generation.<n>We construct long chain-of-thought (CoT) reasoning data, including node selection, workflow planning, and code-level workflow.<n> Experiments show that our 7B- parameter model achieves a 97% format validity rate, along with high pass rate, node-level and graph-level F1 scores.
• Score: 49.09090292453288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-generated content has evolved from monolithic models to modular workflows, particularly on platforms like ComfyUI, enabling customization in creative pipelines. However, crafting effective workflows requires great expertise to orchestrate numerous specialized components, presenting a steep learning curve for users. To address this challenge, we introduce ComfyUI-R1, the first large reasoning model for automated workflow generation. Starting with our curated dataset of 4K workflows, we construct long chain-of-thought (CoT) reasoning data, including node selection, workflow planning, and code-level workflow representation. ComfyUI-R1 is trained through a two-stage framework: (1) CoT fine-tuning for cold start, adapting models to the ComfyUI domain; (2) reinforcement learning for incentivizing reasoning capability, guided by a fine-grained rule-metric hybrid reward, ensuring format validity, structural integrity, and node-level fidelity. Experiments show that our 7B-parameter model achieves a 97\% format validity rate, along with high pass rate, node-level and graph-level F1 scores, significantly surpassing prior state-of-the-art methods that employ leading closed-source models such as GPT-4o and Claude series. Further analysis highlights the critical role of the reasoning process and the advantage of transforming workflows into code. Qualitative comparison reveals our strength in synthesizing intricate workflows with diverse nodes, underscoring the potential of long CoT reasoning in AI art creation.

Related papers

• Policy Optimized Text-to-Image Pipeline Design [72.87655664038617]
  We introduce a novel reinforcement learning-based framework for text-to-image generation.<n>Our approach first trains an ensemble of reward models capable of predicting image quality scores directly from prompt-workflow combinations.<n>We then implement a two-phase training strategy: initial vocabulary training followed by GRPO-based optimization.
  arXiv  Detail & Related papers  (2025-05-27T17:50:47Z)
• Activation-Guided Consensus Merging for Large Language Models [25.68958388022476]
  We present textbfActivation-Guided textbfConsensus textbfMerging (textbfACM), a plug-and-play merging framework that determines layer-specific merging coefficients.<n>Experiments on Long-to-Short (L2S) and general merging tasks demonstrate that ACM consistently outperforms all baseline methods.
  arXiv  Detail & Related papers  (2025-05-20T07:04:01Z)
• ComfyGPT: A Self-Optimizing Multi-Agent System for Comprehensive ComfyUI Workflow Generation [71.31634636156384]
  We introduce ComfyGPT, the first self-optimizing multi-agent system designed to generate ComfyUI based on task descriptions automatically.<n> ComfyGPT comprises four specialized agents: ReformatAgent, FlowAgent, RefineAgent, and ExecuteAgent.<n> FlowDataset is a large-scale dataset containing 13,571 workflow-description pairs, and FlowBench is a benchmark for evaluating workflow generation systems.
  arXiv  Detail & Related papers  (2025-03-22T06:48:50Z)
• Opus: A Large Work Model for Complex Workflow Generation [0.0]
  Opus is a framework for generating and optimizing tasks tailored to complex Business Process Outsourcing (BPO) use cases.<n>Our approach generates executables from Intention, defined as the alignment of Client Input, Client Output and Process Directed Context.
  arXiv  Detail & Related papers  (2024-11-30T20:00:41Z)
• Generating a Low-code Complete Workflow via Task Decomposition and RAG [0.040964539027092926]
  GenAI-based systems are more difficult to design due to their scale and versatility.<n>We formalize two techniques, Task Decomposition and Retrieval-Augmented Generation, as design patterns for GenAI-based systems.<n>As these two patterns affect the entire AI development cycle, we explain how they impacted the dataset creation, model training, model evaluation, and deployment phases.
  arXiv  Detail & Related papers  (2024-11-29T20:13:56Z)
• WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models [105.46456444315693]
  We presentLLM, a data-centric framework to enhance the capability of large language models in workflow orchestration. It first constructs a large-scale fine-tuningBench with 106,763 samples, covering 1,503 APIs from 83 applications across 28 categories. LlamaLlama demonstrates a strong capacity to orchestrate complex APIs, while also achieving notable generalization performance.
  arXiv  Detail & Related papers  (2024-11-08T09:58:02Z)
• Benchmarking Agentic Workflow Generation [80.74757493266057]
  We introduce WorfBench, a unified workflow generation benchmark with multi-faceted scenarios and intricate graph workflow structures.<n>We also present WorfEval, a systemic evaluation protocol utilizing subsequence and subgraph matching algorithms.<n>We observe that the generated can enhance downstream tasks, enabling them to achieve superior performance with less time during inference.
  arXiv  Detail & Related papers  (2024-10-10T12:41:19Z)
• ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation [87.39861573270173]
  We introduce the novel task of prompt-adaptive workflow generation, where the goal is to automatically tailor a workflow to each user prompt. We propose two LLM-based approaches to tackle this task: a tuning-based method that learns from user-preference data, and a training-free method that uses the LLM to select existing flows. Our work shows that prompt-dependent flow prediction offers a new pathway to improving text-to-image generation quality, complementing existing research directions in the field.
  arXiv  Detail & Related papers  (2024-10-02T16:43:24Z)

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.