Update README.md

README.md

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 # Control-LoRA Model Card
 
 
-## Model
-Combining the power of ControlNet and low-rank parameter efficient fine tuning, we introduce Control-LoRAs.
+## Introduction
+What's better than ControlNets for SDXL? ControlNet... but, more efficient.
 
-By integrating the strengths of both approaches, this offers a more efficient and compact method to bring model control for a wider variety of consumer GPUs.
+By introducing low-rank parameter efficient fine tuning, we introduce ***Control-LoRAs***.
+
+Integrating the strengths of both ControlNet and PEFT, this approach offers a more efficient and compact method to bring model control for a wider variety of consumer GPUs.
+
+For each model below, you'll find `Rank 256` files (reducing the `~4.7GB` ControlNets to `~738MB`) and experimental, ultra-pruned `Rank 128` files (reducing to `~377MB`).
 
 ### MiDaS and ClipDrop Depth
 ![canny](samples/depth-sample.jpeg)
 
+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.
+
+In the example above, we compare the depth results of MiDaS dpt_beit_large_512 with ClipDrop Depth for portraits, and their subsequent use in Depth Control-LoRa.
+
+The Control-LoRA utilizes a grayscale depth map for guided generation and has been trained on a diverse range of image concepts and aspect ratios.
+
 ### Canny Edge
 ![canny](samples/canny-sample.jpeg)
 Canny Edge Detection is an image processing technique that identifies abrupt changes in intensity to highlight edges in an image.