Unverified60% confidenceFactExact time
ControlNet was proposed by Zhang Lvmin et al. in 2023; by attaching a trainable encoder branch alongside a pre-trained diffusion model, it receives spatial conditioning signals such as edge maps, depth maps, and semantic segmentation maps to control the structural layout of generated images.
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60%
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Long-term
Relevance
9/9/2026
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UnverifiedControlNet由Zhang等人于2023年提出，通过复制扩散模型编码器权重并加入零卷积层，使深度图、边缘图、骨骼姿态图等条件信号精确控制生成结果78% similarUnverifiedControlNet injects spatial conditioning information without compromising the original model's capabilities by copying the encoder of a pre-trained diffusion model as a trainable copy and connecting it to the original model via zero convolution layers.75% similarVerifiedFill-in-the-Middle (FIM) training provides the model with a code prefix and suffix and asks it to predict the missing middle portion, which more closely resembles real-world code completion scenarios.68% similarUnverifiedControlNet connects a pretrained Stable Diffusion model with a conditioning branch through zero convolution layers, allowing precise control over generated results via additional conditional inputs.68% similarUnverified现代多模态大模型采用统一的端到端训练框架，图像编码器与语言模型联合优化67% similar
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