MakeAnything: Harnessing Diffusion Transformers for Multi-Domain
  Procedural Sequence Generation
Authors: name: Yiren Song, name: Cheng Liu, name: Mike Zheng Shou

A hallmark of human intelligence is the ability to create complex artifacts
through structured multi-step processes. Generating procedural tutorials with
AI is a longstanding but challenging goal, facing three key obstacles: (1)
scarcity of multi-task procedural datasets, (2) maintaining logical continuity
and visual consistency between steps, and (3) generalizing across multiple
domains. To address these challenges, we propose a multi-domain dataset
covering 21 tasks with over 24,000 procedural sequences. Building upon this
foundation, we introduce MakeAnything, a framework based on the diffusion
transformer (DIT), which leverages fine-tuning to activate the in-context
capabilities of DIT for generating consistent procedural sequences. We
introduce asymmetric low-rank adaptation (LoRA) for image generation, which
balances generalization capabilities and task-specific performance by freezing
encoder parameters while adaptively tuning decoder layers. Additionally, our
ReCraft model enables image-to-process generation through spatiotemporal
consistency constraints, allowing static images to be decomposed into plausible
creation sequences. Extensive experiments demonstrate that MakeAnything
surpasses existing methods, setting new performance benchmarks for procedural
generation tasks.