Experience the Magic of SDXL DreamBooth: Free, Fast, and Easy!

Experience the Magic of SDXL DreamBooth: Free, Fast, and Easy!

Table of Contents:

  1. Introduction
  2. Installing Auto Train Advanced
  3. Setting up Auto Train
  4. Training the Model
  5. Generating Images
  6. Adding the Refiner
  7. Training on Google Colab
  8. Training on Hugging Face Spaces
  9. Conclusion

Introduction:

In this video, we will learn how to train a Stable Diffusion Excel model to generate high-quality images. We will cover the process of training the model on your local machine, on Google Colab, and on Hugging Face Spaces. By the end of this tutorial, you will be able to train your own models and generate images using the Stable Diffusion Excel architecture. So, let's get started!

Installing Auto Train Advanced:

Before we begin, make sure You have the latest version of Auto Train Advanced installed. If you already have it installed, it is recommended to upgrade to the latest version to ensure compatibility. To install or upgrade Auto Train Advanced, follow these steps:

  1. Open your command line or terminal.
  2. Run the following command to install or upgrade Auto Train Advanced:
    pip install --upgrade auto-train-advanced
  3. Wait for the installation or upgrade process to complete.

Setting up Auto Train:

Once you have installed or upgraded Auto Train Advanced, you're almost ready to start training your Stable Diffusion Excel model. Follow these steps to set up Auto Train:

  1. Open your command line or terminal.
  2. Run the following command to perform the setup:
    auto-train-setup
  3. Wait for the upgrade process to complete. This will upgrade some packages to the latest version.

Training the Model:

Now that you have Auto Train Advanced set up, you can start training your Stable Diffusion Excel model. Follow these steps to train the model:

  1. Specify the model name by running the following command:

    auto-train-Dreambooth --model stabilityAI/stable-diffusion-excel-base-1.0 --output model-folder

    Replace model-folder with the path where you want the model to be saved.

  2. Prepare your input images by creating a folder and placing the images inside it. Make sure to have at least five images for optimal results.

  3. Run the following command to start the training process:

    auto-train-dreambooth --model stabilityAI/stable-diffusion-excel-base-1.0 --output model-folder --image-path images --prompt "This is a photo of my dog" --resolution 1024 --batch-size 1 --num-inference-steps 25 --enable-fp16 --gradient-accumulation-steps 1 --learning-rate 1e-4

    Adjust the parameters according to your requirements. Higher values for num-inference-steps may result in better image quality, but will also increase the runtime.

  4. Wait for the training process to complete. Once finished, you will find the PyTorch weight saved in the specified model folder.

Generating Images:

After training the model, you can generate images using the trained model. Follow these steps to generate images:

  1. Import the necessary modules by running the following code:

    import torch
    from stable_diffusion.excel import pipeline
    
    model_name = "stabilityAI/stable-diffusion-excel-base-1.0"
    
    pipeline = pipeline.from_pretrained(model_name).to(torch.float16)
  2. Set up the prompt by specifying a description of the image you want to generate, for example:

    
    prompt = "A portrait of my dog"
  3. Generate the images using the trained model by running the following code:

    for seed in range(10):
       generator = torch.Generator().manual_seed(seed)
       image = pipeline.prompt_to_image(prompt, generator=generator, num_inference_steps=25)["image"]
       image.save(f"images/{seed}.jpg")

    Adjust the number of generated images and the image path as per your requirements.

  4. Now you can find the generated images in the specified image folder.

Adding the Refiner:

To enhance the quality of the generated images, you can add the Stable Diffusion Excel Refiner. Follow these steps to add the Refiner:

  1. Import the necessary module by running the following code:

    from stable_diffusion.excel import image_to_image_pipeline
    
    refiner = image_to_image_pipeline.from_pretrained("stabilityAI/stable-diffusion-excel-refiner").to(torch.float16)
  2. Load the trained weights for the Refiner by running the following code:

    refiner_weights = torch.load("model-folder/safe_tensors.pt")
    refiner.load_state_dict(refiner_weights)
  3. Refine the generated images by running the following code:

    refined_images = []
    for image in generated_images:
       refined_image = refiner(image)
       refined_images.append(refined_image)

    Replace generated_images with the list of generated images.

  4. Now you can use the refined images, which will have improved quality.

Training on Google Colab:

If you prefer training your Stable Diffusion Excel model on Google Colab, follow these steps:

  1. Open a new Google Colab notebook and upload your input images to the notebook session.

  2. Run the following command to install Auto Train Advanced:

    !pip install --upgrade auto-train-advanced
  3. Use the Auto Train setup command to upgrade the necessary packages:

    !auto-train-setup
  4. Set the output and image paths according to your requirements and run the Auto Train command to start the training process.

  5. Wait for the training process to complete, and you will find the trained model in the specified output folder.

Training on Hugging Face Spaces:

If you want to train your Stable Diffusion Excel model on Hugging Face Spaces, follow these steps:

  1. Log in to your Hugging Face account and Create a new space.

  2. Choose the Docker option, and select Auto Train from the project types.

  3. Enter the appropriate token and token name, following the instructions provided.

  4. Once your space is ready, go to the settings and choose the appropriate GPU configuration (either 8G small or 18G large).

  5. Change the backend from Hugging Face internal to Hugging Face Spaces.

  6. Select the Computer Vision project Type, and choose DreamBooth.

  7. Upload your input images and set the prompt.

  8. Adjust the other parameters as per your requirements, and then add the job.

  9. Wait for the training process to complete. Once finished, you can find the trained model in your Hugging Face account.

Conclusion:

In this tutorial, we have learned how to train a Stable Diffusion Excel model to generate high-quality images. We covered the process of training the model on your local machine, on Google Colab, and on Hugging Face Spaces. By following the steps outlined in this tutorial, you can now train your own models and create stunning images using Stable Diffusion Excel. So go ahead and give it a try! If you encounter any difficulties or have any questions, feel free to leave a comment. Happy training!

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