Unverified60% confidenceFactExact time
The image generation field is undergoing an architectural paradigm shift from U-Net to Transformer; traditional diffusion models such as Stable Diffusion 1.x and 2.x primarily rely on a U-Net encoder-decoder structure.
2
Sources
60%
Confidence
Long-term
Relevance
8/18/2026
First Seen
Sources
Midjourney --sref风格引用参数详解：一组咒语生成统一奇幻世界
Related Claims
VerifiedDiffusion Transformer (DiT) is the dominant architecture in the current image generation field, used in models such as Stable Diffusion 3 and FLUX.73% similarVerifiedFLUX adopts a DiT (Diffusion Transformer) architecture, replacing the traditional U-Net structure with a pure Transformer.71% similarVerifiedDiffusion Transformer (DiT) is a class of generative backbone networks that introduces the Transformer architecture into diffusion models, cutting noisy images into patches and linearly embedding them into token sequences, and using multi-layer Transformer blocks to predict noise or velocity, replacing the U-Net in traditional diffusion models.71% similarUnverifiedTransformer autoregressive generation is divided into the Prefill stage (compute-intensive, with higher GPU utilization) and the Decode stage (bandwidth-intensive, generating tokens one at a time)70% similarUnverifiedThe DiT architecture replaces the traditional U-Net with a Transformer as the diffusion model backbone, enabling the capture of long-range cross-frame dependencies at the patch level.69% similar
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