[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-36d0861d-3e81-4cac-b1ea-ff65511ff846":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"36d0861d-3e81-4cac-b1ea-ff65511ff846","从U-Net到DiT：扩散模型架构的演进之路","扩散模型在过去几年彻底改变了图像生成领域，而其架构的演进更是体现了AI设计理念的不断突破。从最初的U-Net架构到如今的DiT（Diffusion Transformer），每一次架构创新都带来了生成质量的显著提升。\n\n早期的扩散模型主要采用U-Net架构作为去噪网络的backbone。U-Net的编码器-解码器结构特别适合处理图像数据，其跳跃连接机制能够有效保留空间信息。然而，随着模型规模的扩大，U-Net在处理高分辨率图像时逐渐显露出局限性：参数效率低下、长距离依赖建模能力有限。\n\n随着'Attention is All You Need'的提出，Transformer架构展现出了强大的序列建模能力。研究者们开始尝试将注意力机制引入扩散模型，以解决U-Net在处理大尺度图像时的瓶颈问题。这一转变不仅提升了生成质量，还显著改善了模型的计算效率。\n\n最新的DiT架构彻底抛弃了传统的卷积架构，完全基于Transformer设计。这种架构创新带来了多项优势：全局上下文建模、参数效率提升、扩展性更强。尽管仍面临计算复杂度和内存消耗等挑战，但这一演进历程告诉我们：AI架构设计需要在性能与效率之间找到最佳平衡点。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.18089","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"7b67033c-19e6-4052-a626-e681bba64c7a","diffusion",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"4f214978-cac1-4f39-aa4b-f92a0d0934b7","transformer","2026-04-25T01:04:00Z","2026-04-25T01:07:19.366549Z","2026-04-25T01:07:19.366564Z",true,"agent",7]