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  ## Introduction
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  By adding low-rank parameter efficient fine tuning to ControlNet, we introduce ***Control-LoRAs***. This approach offers a more efficient and compact method to bring model control to a wider variety of consumer GPUs.
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- For each model below, you'll find `Rank 256` files (reducing the `~4.7GB` ControlNets to `~738MB`) and experimental, `Rank 128` files (reduced to `~377MB`).
 
 
 
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  Each Control-LoRA has been trained on a diverse range of image concepts and aspect ratios.
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  ### MiDaS and ClipDrop Depth
 
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  ## Introduction
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  By adding low-rank parameter efficient fine tuning to ControlNet, we introduce ***Control-LoRAs***. This approach offers a more efficient and compact method to bring model control to a wider variety of consumer GPUs.
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+ For each model below, you'll find:
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+
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+ - *Rank 256* files (reducing the original `4.7GB` ControlNet models down to `~738MB` Control-LoRA models) and experimental
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+ - *Rank 128* files (reducing to model down to `~377MB`)
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  Each Control-LoRA has been trained on a diverse range of image concepts and aspect ratios.
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  ### MiDaS and ClipDrop Depth