Ever felt stuck while trying to use one image to create another? Well, worry no more. With the ControlNet-Hough model, you can transform your images easily and effectively. In this guide, we'll walk you through the nitty-gritty of ControlNet-Hough, an AI model that stands at 11th rank on AIModels.fyi. We'll also see how we can use AIModels.fyi to find similar models and decide which one we like. So, buckle up, let's dive right in! About the ControlNet-Hough Model This gem of a model, , is developed by , a prominent figure in the AI modeling world. It uses M-LSD line detection to modify images, essentially creating unique, altered versions of your images. ControlNet-Hough jagilley ControlNet-Hough is in the Image-to-Image category, meaning it takes an image as an input and spits out another image as an output. 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. It's important to note that this model has been run over 4,374,465 times, which speaks volumes about its efficiency and popularity. Understanding the Inputs and Outputs of the ControlNet-Hough Model As we embark on this journey of understanding the model, let's break down its Inputs and Outputs. Inputs The ControlNet-Hough Model takes several inputs, including: : This is the main input, the image you want to modify. image file : Your prompt for the model. prompt string : The number of samples you want (use with caution as higher values may cause out of memory errors). num_samples string : The desired resolution of the generated image. image_resolution string : This pertains to steps. ddim_steps integer : The guidance scale. scale number : This sets the seed for generating the output. seed integer : The value for eta (DDIM). eta number : An added prompt, defaulting to "best quality, extremely detailed". a_prompt string : A negative prompt, defaulting to "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality". n_prompt string : Resolution for detection (only applicable when model type is 'HED', 'Segmentation', or 'MLSD'). detect_resolution integer : Value threshold (only applicable when model type is 'MLSD'). value_threshold number : Distance threshold (only applicable when model type is 'MLSD'). distance_threshold number Outputs The output of this model is an array of URI strings representing the modified images. Now that we're equipped with the knowledge of Inputs and Outputs, let's learn how to interact with this model. Using the ControlNet-Hough Model Whether you're comfortable with code or prefer a more visual approach, there's an option for you. You can directly interact with the model's "demo" on via their UI. But, if you prefer coding, here's how to interact with the ControlNet-Hough Model. Replicate Step 1: Install Replicate First, you need to install the Node.js client by running . npm install replicate Step 2: Copy your API token Next, authenticate by setting your API token as an environment variable: . You can find your API token in your account settings. export REPLICATE_API_TOKEN=<paste-your-token-here> Step 3: Run the model Finally, run the ControlNet-Hough model using the following code: import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

const output = await replicate.run(
  "jagilley/controlnet-hough:854e8727697a057c525cdb45ab037f64ecca770a1769cc52287c2e56472a247b",
  {
    input: {
      image: "..."
    }
  }
); Remember, you can also set a webhook to be called when the prediction is complete. Check out the webhook docs for details on setting that up. Taking it Further - Finding other Image-to-Image Models with AIModels.fyi AIModels.fyi is a fantastic resource for discovering AI models catering to various creative needs, including image generation, image-to-image conversion, and much more. Step 1: Visit AIModels.fyi Head over to AIModels.fyi to begin your search for similar models. Step 2: Use the Search Bar Use the search bar at the top of the page to search for models with specific keywords, such as "image-to-image" or "line detection". This will show you a list of models related to your search query. Step 3: Filter the Results On the results page, you'll find several filters that can help you narrow down the list of models. You can filter and sort by models by type, cost, popularity, or even specific creators. Conclusion In this guide, we explored how to interact with and use the ControlNet-Hough model, either through coding or via a direct UI. We also discussed how to leverage the search and filter features in to find similar models and compare their outputs, allowing us to broaden our horizons in the world of AI-powered image transformation. AIModels.fyi I hope this guide has inspired you to explore the creative possibilities of AI and bring your imagination to life. Don't forget to subscribe for more tutorials, updates on new and improved AI models, and a wealth of inspiration for your next creative project. For more guides and resources, check out the . You can also follow my adventures on . Happy image enhancing and exploring the world of AI with ! notes Twitter AIModels.fyi Also published . here

This story contains new, firsthand information uncovered by the writer.

The code in this story is for educational purposes. The readers are solely responsible for whatever they build with it.

This writer has a vested interest be it monetary, business, or otherwise, with 1 or more of the products or companies mentioned within.

The Beginner's Guide to Line Detection and Image Transformation with ControlNet

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