Dreambooth Ai is now becoming a popular training method. It was developed by Google, and used in their own generic AI model, called Imagen. Since then, the community has known and used Stable Diffusion.

How Does Dreambooth Work?

Dreambooth allows for the teaching of new concepts to Stable Diffusion, with the AI avatar trend being the most popular application. Websites like AvatarAI and ProfilePicture.AI use

Dreambooth to generate images of individuals in various settings. The process involves providing the model with 20-40 images of oneself, which it learns from to generate images that can be used in different scenarios through prompting.

To create personalized images of individuals using Dreambooth, websites, and apps utilize a unique dream booth stable diffusion model for each person. This model is trained specifically for the individual and can generate images of them by using a specific word prompt.

These prompts can generate a vast array of different picture styles, making them an infinite source of image generation.

OpenJourney

At PromptHero, we have extensively experimented with Dreambooth. We utilized this tool to develop OpenJourney, a diffusion model that emulates the style of MidJourney.

OpenJourney is an open-source model that has garnered immense popularity in the AI image generation community, with millions of downloads and significant recognition. Many users prefer OpenJourney over Stable Diffusion, as it produces exceptional images with minimal effort.

If you want to try out OpenJourney, you can access it for free via the PromptHero prompt builder.

In order to create images using OpenJourney, one can add the phrase “mdjrny-v4 style” to their prompt, for instance: “mdjrny-v4 style portrait of a female elf, with intricate and elegant details, in a digital painting format, showcased on ArtStation, with smooth and sharp focus, and illustrated by artists such as Artgerm, Greg Rutkowski, and Alphonse Mucha, in 8K resolution.”

However, it is worth noting that OpenJourney can still generate images effectively even without including the “mdjrny-v4 style” phrase in the prompt.

Limitations of Dreambooth

Dreambooth has its limitations in terms of the number of concepts it can learn and the number of pictures it can handle. The checkpoint that has the most concepts can only accommodate up to 20 concepts.

To ensure effective training, it’s essential to use the available pictures judiciously, either by using a few pictures for each concept or by using one picture to teach multiple concepts.

Teaching Dreambooth multiple concepts with one picture can be challenging as it becomes harder with the increase in the number of things being taught.

Moreover, it’s important to remember the custom words used to identify each concept. Usually, people teach Dreambooth only one thing, and they use 20-40 words to teach it. The resulting model can be quite heavy, often in GBs, such as Stable Diffusion.

Dreambooth Advantages

One clear benefit of Dreambooth is its ability to create images that are not possible with Stable Diffusion. This includes personalized images such as your own face, the protagonists of a new show, or your artwork.

Another advantage is that Dreambooth re-trains the entire model, streamlining the prompt engineering process and resulting in high-quality images with less effort on the user’s part.

Training Dreambooth

Initially, training the model required substantial GPU power, and it could take up to an hour. However, the community has worked on enhancing the model, reducing the GPU requirements, and decreasing training time.

Presently, it is possible to utilize Dreambooth to train Stable Diffusion on a free Google Colab notebook, which takes approximately 15 minutes. The Colab notebook provides comprehensive guidelines on how to run the program, enabling you to create your fine-tuned Dreambooth model.

Fine-tuning a good model is a challenging topic that delves into technical aspects beyond what we’ve covered so far.

In order to use Dreambooth proficiently, we recommend that you always persevere in testing to get the desired results.