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Neural network designs that use frozen weights to constrain or guide base models.
Distinguishing note: Focuses on the use of frozen, mirrored networks for constraint injection.
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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
Injects structural constraints using a frozen, weight-locked copy of the base architecture.