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

TL;DR

CookAnything is a diffusion-based framework that generates coherent, multi-step recipe images from arbitrary-length textual instructions, addressing limitations of previous models in handling structured, variable-length procedures.

Contribution

The paper introduces three novel components—SRC, Flexible RoPE, and CSCC—that enable flexible, consistent, and semantically aligned multi-step recipe image generation.

Findings

Outperforms existing methods on recipe illustration benchmarks.

Supports scalable, high-quality image synthesis for complex instructions.

Maintains ingredient consistency across steps.

Abstract

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.