ComfyUI-R1: Exploring Reasoning Models for Workflow Generation

TL;DR

ComfyUI-R1 is a large reasoning model designed to automate the generation of complex workflows in AI content creation platforms, significantly reducing the expertise needed and improving validity and fidelity.

Contribution

The paper introduces ComfyUI-R1, the first large reasoning model for automated workflow generation, utilizing a two-stage training process with chain-of-thought fine-tuning and reinforcement learning.

Findings

Achieves 97% format validity rate.

Surpasses state-of-the-art models like GPT-4o and Claude.

Effectively synthesizes complex workflows with diverse nodes.

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.