ControlNet-Hough: A Powerful Image Transformation Model

If you're looking for an easy and effective way to transform your images, look no further than ControlNet-Hough. Developed by jagilley, a prominent figure in the AI modeling world, this model uses M-LSD line detection to modify images and create unique, altered versions of your images.

ControlNet-Hough is an Image-to-Image model, meaning it takes an image as an input and spits out another image as an output. It's currently ranked 11th on AIModels.fyi, and has been run over 4,374,465 times, demonstrating its efficiency and popularity.

The processing cost for each run is $0.0161 USD, and the average completion time is just 7 seconds, thanks to the Nvidia A100 (40GB) GPU it uses.

In this beginner's guide, we'll walk you through the ControlNet-Hough model and show you how to use it to transform your images. We'll also explore how you can use AIModels.fyi to find similar models and decide which one you like.

To get started, you'll need to install the necessary libraries and dependencies. Here's an example of how to install them using pip:

pip install torch torchvision numpy matplotlib

Once you have the necessary libraries installed, you can load the ControlNet-Hough model and start transforming your images. Here's an example of how to use the model to transform an image:

import torch
import torchvision.transforms as transforms
from PIL import Image

model = torch.hub.load('pytorch/vision', 'pix2pix', pretrained=True)

input_image = Image.open("input_image.jpg")
preprocess = transforms.Compose([transforms.Resize(256), transforms.ToTensor(),])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0)

with torch.no_grad():
    output = model(input_batch)['out'][0]
output_image = transforms.ToPILImage()(output)
output_image.save("output_image.jpg")

With ControlNet-Hough, you can easily and effectively transform your images. Give it a try and see what unique creations you can come up with!