What is Stable Diffusion Textual Inversion?

Stable Diffusion Textual Inversion is a technique that allows you to add new styles or objects to your text-to-image models without modifying the underlying model. It works by defining a new keyword representing the desired concept and finding the corresponding embedding vector within the language model.

How do I train a Stable Diffusion Textual Inversion model?

To train a Stable Diffusion Textual Inversion model, you'll need to gather a set of images that represent the concept you want to add to your model. Once you have your images, you can use a tool like AUTOMATIC1111’s Stable Diffusion WebUI to train your model. The process involves feeding your images into the model and allowing it to learn the patterns and features that make up your concept.

The Ulitmate Stable Diffusion Textual Inversion Guide

Name: Naomi Clarkson

Updated on 8/17/2023

A comprehensive guide to fine-tuning Stable Diffusion for textual inversion. Learn how to add new styles or objects to your text-to-image models without modifying the underlying model.

Welcome to our comprehensive guide on Stable Diffusion Textual Inversion. In this guide, we will explore how to fine-tune Stable Diffusion for textual inversion, a powerful technique for capturing novel concepts from a small number of example images. This process allows for personalized image generation, offering a new level of control over the images generated from text-to-image pipelines.

Stable Diffusion, a potent latent text-to-image diffusion model, has revolutionized the way we generate images from text. With the addition of textual inversion, we can now add new styles or objects to these models without modifying the underlying model. This guide will provide you with a step-by-step process to train your own model using textual inversion.

What is Textual Inversion in Stable Diffusion?

Textual inversion is a technique that allows us to add new styles or objects to text-to-image models without modifying the underlying model. It involves defining a new keyword representing the desired concept and finding the corresponding embedding vector within the language model. This technique enables the model to generate images based on the user-provided concept, often requiring as few as 3-5 sample images.

For instance, if you want to generate an image of a

"robot drawing in the wild, nature, jungle"

You can define a new keyword "robot-art" and find its corresponding embedding vector. The model will then generate an image based on this concept.

The process enables personalized creation through the composition of natural language sentences using these new “words” in the embedding space of the model. A single-word embedding is often enough to capture diverse and distinct concepts. Textual inversion (embeddings) files are typically 10-100KB in size and use *.pt or *.safetensors file extension.

How to Add Textual Inversion to Stable Diffusion?

Adding textual inversion to Stable Diffusion involves a few steps. First, you need to download a textual inversion (embedding) file. The best places to find these files are Civitai and Hugging Face. After downloading the file, place it in the appropriate folder if you're using a tool like AUTOMATIC1111’s Stable Diffusion WebUI (opens in a new tab).

The textual inversions work with a keyword or trigger word. This trigger word is usually shown to you in the same place where you've downloaded the embedding. Use these trigger words in your text prompt to activate the textual inversion during the image-generation process.

For example, if you're using AUTOMATIC1111’s WebUI, you can:

Click the little “image” icon under the Generate button to show available textual inversions.
When you click the Textual inversion, it will be applied to the correct text prompt.
If your trigger word is "robot-art", you can include this in your text prompt like "Generate an image with robot-art".

How Many Images are Needed for Stable Diffusion Textual Inversion?

Remarkably, textual inversion can achieve its goal with as few as 3-5 sample images. The process enables personalized creation through the composition of natural language sentences using these new “words” in the embedding space of the model. A single-word embedding is often enough to capture diverse and distinct concepts.

For example, let's say you want to generate an image of a "beach sunset". With just a few sample images of beach sunsets, you can train the model to understand this concept. Your text prompt can be something like:

Sample prompt: "Generate an image of a beach sunset."

Similarly, if you want to generate an image of a "floral pattern", you can use a few sample images of floral patterns to train the model. Your text prompt can be:

Sample prompt: "Generate an image with a floral pattern."

By providing these sample prompts, the model can understand the desired concept and generate images accordingly.

However, while textual inversion generally works well with a small number of sample images, it's important to note that the quality and diversity of the images can impact the output. Using a larger and more diverse dataset can help improve the model's ability to generate accurate and creative images.

How to Train Your Face in Stable Diffusion?

How to Train Your Face in Stable Diffusion

Training your face in Stable Diffusion involves a similar process to textual inversion. First, you need to gather a set of images of your face. These images should be diverse, covering different angles, expressions, and lighting conditions. The more varied your dataset, the better the model will be at generating new images that capture your likeness.

Once you have your dataset, you can use a tool like AUTOMATIC1111’s Stable Diffusion WebUI to train your model. The process involves feeding your images into the model and allowing it to learn the patterns and features that make up your face. This is done through a process called fine-tuning, where the model's existing knowledge is adjusted to better fit the new data.

Here are some key factors you need to consider during the process:

Negative Prompt: Exclude specific elements or concepts from the generated images.
Seed: Determine randomness in image generation.
Number of Images: Choose the total number of images you wish to create.
Model Selection: Opt for different models for generating diverse results.
Image Size: Control the dimensions of the output images.
Guidance Scale: Adjust the level of adherence to the prompt.
Image Modifiers: Utilize additional tools to refine and enhance your prompts.

For example, if you want the model to generate images of you smiling, you might use a text prompt like

"Generate an image with my-smile".

The model would then generate an image based on the concept of "my-smile", which it learned during the fine-tuning process.

Stable Diffusion Textual Inversion Download

Downloading textual inversion for Stable Diffusion is a straightforward process. The best places to find these files are Civitai and Hugging Face. These platforms host a variety of textual inversion files that you can use to add new styles or objects to your text-to-image models.

Once you've found a textual inversion file that suits your needs, simply download it and place it in the appropriate folder. If you're using a tool like AUTOMATIC1111’s Stable Diffusion WebUI, this would be this folder:

*\stable-diffusion-webui\embeddings

For example, if you downloaded a textual inversion file for "robot-art", you would place this file in the embeddings folder. Then, when you want to generate an image based on this concept, you can use a text prompt like "Generate an image with robot-art".

Where to Put Textual Inversion Stable Diffusion?

Once you've downloaded a textual inversion file, the next step is to put it in the correct location. If you're using a tool like AUTOMATIC1111’s Stable Diffusion WebUI, you should place the file in this folder:

*\stable-diffusion-webui\embeddings

It's important to note that the textual inversion file should match the format expected by the tool you're using. Most textual inversion files use the *.pt or *.safetensors file extension. If your file is in a different format, you may need to convert it before it can be used.

Want to write great Stable Diffusion Prompts? You can read out Stable Diffusion prompt guide to get started!

FAQ

What is Stable Diffusion Textual Inversion? Stable Diffusion Textual Inversion is a technique that allows you to add new styles or objects to your text-to-image models without modifying the underlying model. It works by defining a new keyword representing the desired concept and finding the corresponding embedding vector within the language model.
How do I train a Stable Diffusion Textual Inversion model? To train a Stable Diffusion Textual Inversion model, you'll need to gather a set of images that represent the concept you want to add to your model. Once you have your images, you can use a tool like AUTOMATIC1111’s Stable Diffusion WebUI to train your model. The process involves feeding your images into the model and allowing it to learn the patterns and features that make up your concept.
Where can I download Textual Inversion files? You can download Textual Inversion files from platforms like Civitai and Hugging Face. These platforms host a variety of textual inversion files that you can use to add new styles or objects to your text-to-image models.