MakeAnything Unlocks Multi-task Process Generation with Diffusion Transformer
MakeAnything combines Diffusion Transformer and asymmetric LoRA to unlock cross-domain, high-quality multi-task process generation, achieving exceptional results across 21 tasks.
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One major feature of human intelligence is the ability to create complex works step by step, such as painting, crafts, and cooking, where logic and aesthetics come together in the process.
However, teaching AI to generate such “step-by-step tutorials” faces three major challenges: the scarcity of multi-task data, insufficient logical consistency between steps, and limited cross-domain generalization ability.
A recent study from the National University of Singapore, MakeAnything, combines Diffusion Transformer (DiT) with asymmetric LoRA technology, achieving high-quality, cross-domain procedural sequence generation for the first time. It performs excellently across 21 task categories, demonstrating outstanding generalization on new tasks. This article delves into the design and experimental results of this technology.