Stable Diffusion Tutorial Part 2: Using Textual Inversion Embeddings to gain substantial control over your generated images

In our previous tutorial, we demonstrated how to use DreamBooth with Stable Diffusion to fine-tune a model and create a consistent baseline concept—enabling it to better generate images that reflect a specific object or artistic style from a set of input images. While other fine-tuning approaches, such as using Guided Diffusion with glid-3-XL-stable, have also shown promising results, they tend to be extremely resource-intensive and require high-end data center GPUs to run.

DreamBooth, on the other hand, offers a much more efficient alternative, needing only 16 GB of GPU RAM—dramatically lowering the hardware requirements. Thanks to this, users can now leverage cloud-based solutions like DigitalOcean GPU Droplets to run DreamBooth efficiently without investing in expensive hardware. This opens up a much more accessible and budget-friendly pathway for creators and developers to explore the growing world of Stable Diffusion and custom AI-generated content.

Another popular technique for achieving similar results is Textual Inversion. While it is also computationally intensive, it offers a valuable alternative for customizing image generation. Despite the name, Textual Inversion doesn’t fine-tune the diffusion model itself. Instead, it teaches the model to associate new, user-defined concepts—such as personal objects or unique artistic styles—with newly created tokens in the model’s embedding space.

These tokens act like words that represent the concept and can be used in prompts just like any other word. This gives users a different kind of control over the image generation process—one that focuses on precision and flexibility in crafting textual prompts. When used alongside DreamBooth-trained concepts, Textual Inversion enhances the inference process by combining visual specificity with linguistic nuance, enabling more accurate and expressive outputs.

In this tutorial, we will show how to train Textual Inversion on a pre-made set of images from the same data source we used for Dreambooth. Once we have walked through the code, we will demonstrate how to combine our new embedding with our DreamBooth concept in the Stable Diffusion Web UI launched from a Jupyter Notebook.