CookAnything：灵活一致的多步骤食谱图像生成框架

CookAnything: A Framework for Flexible and Consistent Multi-Step Recipe Image Generation

December 3, 2025

作者: Ruoxuan Zhang, Bin Wen, Hongxia Xie, Yi Yao, Songhan Zuo, Jian-Yu Jiang-Lin, Hong-Han Shuai, Wen-Huang Cheng

cs.AI

摘要

烹饪是一项具有时序性和视觉基础的活动，其中切菜、搅拌、煎炒等每个步骤都蕴含着操作逻辑与视觉语义。尽管当前扩散模型在文生图领域展现出强大能力，但难以处理如食谱图解这类结构化多步骤场景。现有食谱插图方法还存在适应性缺陷——无论实际操作步骤如何变化，都只能生成固定数量的图像。为突破这些限制，我们提出CookAnything框架：这是一个基于扩散模型的灵活系统，能够根据任意长度的文本烹饪指令生成语义连贯且视觉区分度高的图像序列。该框架包含三大核心组件：（1）步骤区域控制技术，在单次去噪过程中实现文本步骤与对应图像区域的对齐；（2）柔性RoPE位置编码机制，通过步骤感知增强时序连贯性与空间多样性；（3）跨步骤一致性控制模块，确保食材细节在不同步骤间保持统一。在食谱插图基准测试中，CookAnything在训练依赖与零样本设置下均优于现有方法。该框架支持对复杂多步骤指令进行可扩展的高质量视觉合成，在教学媒体和流程化内容创作领域具有广阔应用前景。

English

Cooking is a sequential and visually grounded activity, where each step such as chopping, mixing, or frying carries both procedural logic and visual semantics. While recent diffusion models have shown strong capabilities in text-to-image generation, they struggle to handle structured multi-step scenarios like recipe illustration. Additionally, current recipe illustration methods are unable to adjust to the natural variability in recipe length, generating a fixed number of images regardless of the actual instructions structure. To address these limitations, we present CookAnything, a flexible and consistent diffusion-based framework that generates coherent, semantically distinct image sequences from textual cooking instructions of arbitrary length. The framework introduces three key components: (1) Step-wise Regional Control (SRC), which aligns textual steps with corresponding image regions within a single denoising process; (2) Flexible RoPE, a step-aware positional encoding mechanism that enhances both temporal coherence and spatial diversity; and (3) Cross-Step Consistency Control (CSCC), which maintains fine-grained ingredient consistency across steps. Experimental results on recipe illustration benchmarks show that CookAnything performs better than existing methods in training-based and training-free settings. The proposed framework supports scalable, high-quality visual synthesis of complex multi-step instructions and holds significant potential for broad applications in instructional media, and procedural content creation.