CookAnything: A Framework for Flexible and Consistent Multi-Step Recipe Image Generation
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.
6930f5462d1e5b0a7d84d97c
Ruoxuan Zhang
6930f5462d1e5b0a7d84d97d
Bin Wen
6930f5462d1e5b0a7d84d97e
Hongxia Xie
6930f5462d1e5b0a7d84d97f
Yi Yao
6930f5462d1e5b0a7d84d980
Songhan Zuo
6930f5462d1e5b0a7d84d981
Jian-Yu Jiang-Lin
6930f5462d1e5b0a7d84d982
Hong-Han Shuai
6930f5462d1e5b0a7d84d983
Wen-Huang Cheng
admin_assigned
2025-12-04T11:12:06.125Z
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/avatars/420a97204d144db2c6aa3215cab1b9fb.svg
Wen-Huang Cheng
twbear2024
user
twbear2024
CookAnything: A Framework for Flexible and Consistent Multi-Step Recipe Image Generation
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.
CookAnything is a diffusion-based framework that generates coherent image sequences from cooking instructions by incorporating step-wise regional control, flexible positional encoding, and cross-step consistency.
diffusion models Step-wise Regional Control Flexible RoPE Cross-Step Consistency Control recipe illustration denoising process temporal coherence spatial diversity