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.
For each model below, you'll find:
Rank 256 files (reducing the original 4.7GB ControlNet models down to ~738MB Control-LoRA models) and experimental
Rank 128 files (reducing to model down to ~377MB)
Each Control-LoRA has been trained on a diverse range of image concepts and aspect ratios.
MiDaS and ClipDrop Depth
canny
This Control-LoRA utilizes a grayscale depth map for guided generation.
Depth estimation is an image processing technique that determines the distance of objects in a scene, providing a depth map that highlights variations in proximity.
The model was trained on the depth results of MiDaS dpt_beit_large_512.
