CookAnything: 一个灵活且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
AI总结：
CookAnything通过引入步骤式区域控制、灵活RoPE和跨步骤一致性控制，实现了灵活且一致的多步骤食谱图像生成，提升复杂烹饪指令的视觉合成质量。
摘要：
烹饪是一种顺序且视觉基础的活动，其中每个步骤如切菜、混合或煎炒都包含过程逻辑和视觉语义。尽管最近的扩散模型在文本到图像生成方面表现出强大的能力，但它们在处理结构化的多步骤场景如食谱示意图时却显得力不从心。此外，当前的食谱示意图方法无法适应食谱长度的自然变化，生成固定数量的图像，无论实际的指导结构如何。为了解决这些限制，我们提出了CookAnything，一个灵活且一致的扩散基框架，能够从任意长度的文本烹饪说明中生成连贯且语义不同的图像序列。该框架引入了三个关键组件：(1) 步骤式区域控制（SRC），在单个去噪过程中将文本步骤与对应的图像区域对齐；(2) 灵活的RoPE，一种步骤感知的位置编码机制，能够增强时间一致性和空间多样性；(3) 跨步骤一致性控制（CSCC），在步骤间保持细粒度的原料一致性。在食谱示意图基准上的实验结果表明，CookAnything在基于训练和无训练的设置中均优于现有方法。所提出的框架支持大规模、高质量的复杂多步骤指令的视觉合成，并在教学媒体和过程内容创作中具有广泛的应用潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
