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ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation

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arxiv 2410.01731 v1 pith:JMIVPW7L submitted 2024-10-02 cs.CV cs.CLcs.GR

Rinon Gal , Adi Haviv , Yuval Alaluf , Amit H. Bermano , Daniel Cohen-Or , Gal Chechik This is my paper · ORCID
classification cs.CVcs.CLcs.GR
keywords generationworkflowsapproachesqualitytext-to-imagecomplexcomponentsexisting
verification ladder T0 review T1 audit T2 compute T3 formal
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The practical use of text-to-image generation has evolved from simple, monolithic models to complex workflows that combine multiple specialized components. While workflow-based approaches can lead to improved image quality, crafting effective workflows requires significant expertise, owing to the large number of available components, their complex inter-dependence, and their dependence on the generation prompt. Here, 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. Both approaches lead to improved image quality when compared to monolithic models or generic, prompt-independent workflows. 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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Knowledge-Centric Agents for Workflow Generation in ComfyUI

    cs.AI 2026-07 conditional novelty 6.0 of 10

    A knowledge-centric pipeline distills strategies and pseudo-codes from real workflows, fine-tunes a language model on those levels, and reconstructs executable ComfyUI graphs from task descriptions.

  2. AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries?

    cs.DB 2025-08 unverdicted novelty 6.0 of 10

    A benchmark organized by a six-type taxonomy of ambiguous graph queries reportedly shows that nine LLMs, including top models, frequently produce wrong query translations.

  3. ComfyUI-Copilot: An Intelligent Assistant for Automated Workflow Development

    cs.CL 2025-06 conditional novelty 5.0 of 10

    An LLM-powered multi-agent Copilot retrieves and constructs ComfyUI workflows, reporting at least 88.5% recall on its own test set and 85.9% online acceptance of proposed workflows.

  4. ComfyMind: Toward General-Purpose Generation via Tree-Based Planning and Reactive Feedback

    cs.AI 2025-05 conditional novelty 5.0 of 10

    A ComfyUI-based multi-agent system with semantic workflow modules and tree-based local-feedback planning reports near-perfect pass rates on ComfyBench and competitive scores on GenEval and Reason-Edit.

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