Put Yourself INSIDE Stable Diffusion

5 Mar 202311:36

TLDRThis tutorial demonstrates how to integrate one's face into Stable Diffusion for personalized image generation. It guides through creating a dataset of 512x512 resolution images, setting up an embedding with a unique name, and training the model with specified learning rate and batch size. The process involves selecting a prompt template, iterating over the images, and updating the embedding for improved results. The outcome is a model capable of generating images that closely resemble the individual, which can be further refined by adjusting training parameters and using different styles or prompts.


  • 📸 Start by gathering a dataset of high-resolution images (512x512) of the face you want to use with Stable Diffusion.
  • 🔄 Ensure variety in the dataset with different poses, environments, and lighting conditions for better model training.
  • 🌟 Create an embedding unique to your dataset by naming it and setting the number of vectors per token (between 3 and 4 is recommended).
  • 📝 Select an appropriate embedding learning rate (e.g., 0.005) for precise and fine-tuned training.
  • 💻 Adjust the batch size according to your GPU's capability, with a minimum of 1 and a maximum that your hardware can handle.
  • 🗂️ Use the images from your dataset by copying the folder directory and pasting it into the training panel.
  • 📄 Choose a prompt template (subject file) for training, which will guide the model during the generation process.
  • 🔢 Set the number of training steps (e.g., 3000) and specify the frequency of image output and embedding updates (every 25 iterations).
  • 🖼️ After training, use the generated embeddings to create images by typing the unique name into the Stable Diffusion text-to-image feature.
  • 🎨 Experiment with different styles and prompts to refine the output, such as 'in the style of Van Gogh' or 'as a painting'.
  • 🔄 Continue training and updating embeddings for better results over time, avoiding overtraining while improving the model's accuracy.

Q & A

  • What is the main topic of the tutorial?

    -The main topic of the tutorial is how to use Stable Diffusion to create images using one's own face or someone else's face with a dataset of their face.

  • What type of images are required for the dataset?

    -The images required for the dataset should be 512 by 512 pixels in resolution.

  • Why is it important to have different poses and environments in the dataset?

    -Having different poses and environments in the dataset helps the model to better understand and generate more accurate and diverse images of the person.

  • What is the significance of creating an embedding in Stable Diffusion?

    -Creating an embedding in Stable Diffusion is important because it allows the model to recognize and generate images of the specific person whose face dataset is being used.

  • How does one name their embedding in the tutorial?

    -In the tutorial, the user named their embedding 'Tom tutorial', but it's advised to choose a unique, one-word name that is memorable and not already used by another dataset.

  • What is the role of the embedding learning rate in the training process?

    -The embedding learning rate determines the speed and precision of the training process. A smaller number, like 0.005, will result in a slower but more fine-tuned training process.

  • What is the purpose of the prompt template in training?

    -The prompt template is used to guide the training process by providing a consistent message or theme, such as 'portrait of a blank', which helps the model understand what kind of images to generate.

  • How often should the model generate an image during training to evaluate its progress?

    -The model should generate an image and update the embedding every 25 iterations to evaluate its progress and refine the training.

  • What is the recommended number of iterations for sufficient training?

    -While it varies, a number of people like to use 3000 iterations, but it's important not to overtrain the model as it can become excessive and not yield better results.

  • How can one use the trained embedding for generating images?

    -After training the embedding, one can use it in the 'text to image' feature of Stable Diffusion, typing in the name of the embedding followed by a prompt, such as 'portrait of a Tom tutorial', to generate an image.

  • What adjustments can be made to improve the generated images?

    -Adjustments such as changing the style, using different prompts, or adding negative prompts to remove unwanted elements from the generated images can be made to improve their accuracy and resemblance to the person in the dataset.



🖼️ Introduction to Stable Diffusion Tutorial

The paragraph introduces a tutorial on using stable diffusion with one's own face or someone else's, given that a dataset of facial images is available. The speaker emphasizes the need for images to be 512 by 512 resolution and discusses the importance of having a variety of poses and environments in the dataset. The process of embedding oneself into the model is mentioned, as well as the need to create a unique name for this embedding. The speaker also touches on the complexity of other tutorials and aims to provide a streamlined, easy-to-follow guide.


🛠️ Setting Up the Training Process

This paragraph delves into the specifics of setting up the training process for the stable diffusion model. It covers the process of creating an embedding, selecting a learning rate, and determining the batch size based on the capabilities of one's GPU. The speaker also explains the importance of not over-training the model and provides guidance on how often to generate images to monitor the training progress. The paragraph further discusses the selection of a prompt template, with a focus on using a subject file for training, and shares the speaker's personal choices and experiences with the process.


🚀 Observing Training Progress and Results

The speaker shares observations from the training process, noting that the model's output improves with each iteration. They demonstrate how to save and update the embedding at set intervals and how to use the trained embedding to generate images. The paragraph includes examples of the types of results one might expect at various stages of training, from initial vague resemblances to more refined portraits. The speaker also explores different styles and settings within the stable diffusion model, such as creating a painting or a Lego version of themselves, and provides tips on how to adjust prompts to achieve better results.



💡Stable Diffusion

Stable Diffusion is a term used in the context of machine learning and artificial intelligence, referring to a model that generates images from textual descriptions. In the video, it is the primary tool used to create visual outputs based on a dataset of images. The model is trained with a specific dataset, in this case, the speaker's own images, to generate pictures that resemble the subject of the dataset.

💡Data Set

A dataset, in this context, is a collection of images used to train the Stable Diffusion model. The speaker emphasizes the importance of having a dataset with a variety of poses, environments, and lighting conditions to improve the model's ability to generate accurate images.


In the context of the video, embedding refers to the process of incorporating the speaker's identity into the Stable Diffusion model. This is done by creating a unique identifier that represents the speaker's dataset, allowing the model to generate images based on that specific identity.


Training, in the context of this video, is the process of teaching the Stable Diffusion model to recognize and generate images based on the new embedding. This involves adjusting various settings and allowing the model to iterate over the dataset multiple times to improve its accuracy.

💡Prompt Template

A prompt template is a textual guide used by the Stable Diffusion model to generate images. It includes specific instructions or descriptions that the model uses to create the visual output. In the video, the speaker uses a 'subject' template to train the model to generate images of themselves.

💡Learning Rate

The learning rate is a hyperparameter in machine learning models that determines the step size at which the model adjusts its parameters during the training process. A smaller learning rate means the model will learn more slowly but potentially with more precision. In the video, the speaker sets an embedding learning rate of 0.002 for training their Stable Diffusion model.

💡Batch Size

Batch size refers to the number of images the model processes at one time during training. A larger batch size means more images are considered simultaneously, but it also requires more computational resources. The speaker in the video uses a batch size of eight, which is determined by their GPU's capabilities.


Iterations are the repeated cycles of training the model goes through while learning from the dataset. Each iteration involves the model generating an image based on the current understanding of the dataset. The speaker in the video sets the model to generate an image and update the embedding every 25 iterations.

💡Textual Inversion

Textual inversion is a process in the training of AI models where textual descriptions are used to guide the generation of images. In the context of the video, it refers to the folder where the training of the Stable Diffusion model takes place, using textual prompts to produce visual outputs.


In the context of the video, style refers to the artistic or visual characteristics that the Stable Diffusion model can mimic when generating images. The speaker experiments with different styles, such as turning their image into a painting or applying the style of a specific artist like Van Gogh.


In the video, Legos are used as an example of a creative and challenging prompt for the Stable Diffusion model. The speaker asks the model to generate an image of themselves as if made out of Legos, which requires the model to interpret and visualize the speaker's face using the distinctive appearance of Lego bricks.


The tutorial provides a step-by-step guide on using Stable Diffusion with a personal dataset.

A dataset of 512x512 images is recommended for optimal results with Stable Diffusion.

Diverse poses, environments, and lighting conditions in the dataset can improve the training outcome.

Creating an embedding is essential to include oneself in the Stable Diffusion model.

The embedding name must be unique and should not overlap with existing names in the model.

The number of vectors per token can be adjusted based on the size of the image dataset.

The training process involves setting an embedding learning rate and batch size.

The data set folder directory must be provided for the training to use the correct images.

A prompt template is selected for the training, with the subject file being particularly important.

The model is trained over multiple iterations, with images generated at set intervals for review.

After training, the embedding can be replaced with a newer version for improved results.

The training process can be resumed later by loading the saved embedding and continuing from the last step.

The generated images will gradually improve in likeness and accuracy as training progresses.

Different styles and themes can be applied to the generated images for creative outputs.

The tutorial demonstrates the potential of Stable Diffusion for personalized content creation.

The process of embedding oneself in Stable Diffusion opens up possibilities for custom AI-generated art.

The tutorial concludes with a showcase of the improved results after extensive training.