Unverified50% confidenceFactExact time
The core idea of DiT is to split the image into patches and treat them as a sequence input to the Transformer, and to inject timestep and class conditions through adaptive layer normalization (adaLN).
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50%
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Long-term
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10/26/2026
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Related Claims
UnverifiedThe latest generation of editing models adopts the DiT (Diffusion Transformer) architecture, which processes image tokens and text tokens in a unified manner, thereby achieving finer cross-modal alignment.75% similarUnverifiedThe patch-based input approach is inspired by the Vision Transformer's idea of dividing images into patches.72% similarUnverifiedMeta's Muse Image adopts a Transformer-based masked generation architecture and has a significant advantage in image generation speed.71% similarUnverifiedTransformer架构被引入视觉生成（如Vision Transformer、DiT架构），使模型能更好理解图像不同区域间的全局关系，适用于多模态场景中文本token和图像token的统一处理71% similar
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