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Trainable layers initialized to zero to prevent interference during model fine-tuning.
Distinguishing note: Specifically targets the zero-convolutional injection technique.
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ControlNet is a framework for structural image generation that extends pre-trained diffusion models with neural network architectures designed for precise spatial control. By injecting structural guidance directly into the latent-space denoising process, the system enables users to enforce geometric or semantic constraints on generated outputs while maintaining style consistency. The framework distinguishes itself through a weight-locked copying mechanism that preserves the integrity of the original model while introducing new control signals. It supports multi-condition synthesis, allowing f
Uses zero-initialized layers to gradually introduce control signals without disrupting pre-trained weights.