How to Train Stable Diffusion Like No Other

Stable diffusion turns a text prompt into an image by generating the image it thinks is closest to that description. The model is biased toward images that match a specific dataset, so it may need to be fine-tuned.

This can be done by adding new training images that the model has never seen before. This is a common machine learning approach.

Training

If you are looking to train an embedding for a specific subject or artistic style, the first step is to find training images. These can be photos or drawn pictures. It is generally recommended that you have at least 20 to 50 image examples to train the model well. Once you have your training images, it is important to normalize them. This rescales the pixel values to fit within the [-1, 1] range that Stable Diffusion expects. Then, you can create a DataLoader to load these images into the model for training.

Once you have the image data loaded into your model, you can run a training job to create an embedding for that subject or artistic style. The default initialization text is “*” and you should replace it with the name of your subject or artistic style. This will be the name that you use in your text prompts to trigger use of the embedded model. You should also change the number of vectors per token (again, a default value is 1) to suit your subject or art style.

The training job will take some time to complete, but once it does you will be able to create an embedding for your subject or artistic style. When you are creating the embedding, make sure to set the Initialization text and Negative Prompt to the same name as you used in the training job. This will be how you judge whether the model is producing a good result.

In addition to training your own stable diffusion model, there are many pre-trained models available that are specialized for different aesthetics. Dreamshaper is one example that has been fine-tuned for a portrait illustration style that sits between photorealistic and computer graphics. Deliberate v2 is another great model that produces realistic illustrations.

Stable Diffusion 2.1 has been improved significantly in terms of its ability to generate artistic styles. It has also been made more robust in avoiding pornographic content, which was a problem with the 2.0 model.

Last year, Simo Ryu introduced a technique called DreamBooth that allows you to train a stable diffusion model on your own objects or style and then use it to generate new images. More recently, he has developed a new image generation model called LoRA that takes this a step further. LoRA is fast and easy to use.

Fine-tuning

The process of fine-tuning a stable diffusion model is to take an existing model and tweak it to better match your specific task. For example, if you want to create an image of your cat on the moon, you will need to train the model with a specific set of images containing your cat in different poses and locations.

This is a common practice with all types of models including image classification networks and GANs. For example, you can use an already-trained model with a special dataset to generate Pokemon inspired images from any text prompt. This is a very popular application of the model and there are plenty of examples to try out online.

To get started, you will need a Google account and access to the Determined repository. First, clone the repository and select a location to save your files. Next, select the “Instructions” tab and click “Start Experiment.” Once your experiment has started, you can view training progress in the logs. Note that training may take anywhere from 15 minutes to more than an hour, so be patient and check back on the progress occasionally.

When the training is complete, you will need to convert the model to the ckpt format. The model will be automatically converted to this format when the experiment is finished, but you can also manually convert it using the command line if necessary.

Once you have the ckpt file, you can start generating images from it. The simplest way to do this is by opening the ckpt file in an image editor like Photoshop and selecting your desired color and lighting. You can then drag and drop the resulting image into your project to display it.

Another way to generate images is by using a text-to-image model, like Stable Diffusion, Midjourney, or Dalle2. These models recognize words and concepts and styles and ‘draw’ them. They can only draw things that they have been trained to recognize, however, so you will need to train them with the subject that you are trying to generate images of.

Visualization

Stable diffusion is a generative model that tries to generate new data (in this case, images) similar to what it has seen in training. It’s named for the mathematical pattern it uses to predict how an attribute or information will spread and mix in a system. The model is able to create complex patterns, like those shown in the illustration above, and is incredibly effective at it. It can also be used to forecast the future behavior of systems, like financial markets or chemical reactions.

The first step in using stable diffusion is creating a text prompt, which is a string of words that describe the subject you want the model to depict. Then, click the Train Embedding button to begin the process of training a new custom embedding for your prompt. The resulting custom embedding is what Stable Diffusion will use to generate the image you see.

When you’re done, click the Stop button. It will save the results of the training to a log directory in your textual inversion folder. This is important because it will allow you to see the progress made during training, and will help you identify if any errors occurred.

You’ll notice that by default, the model will produce square images. If you’d prefer rectangular images, you can adjust the height and width arguments to change this. You’ll also notice that the image size is 512×512 pixels. This is the default for all models, and it’s a great choice because it allows you to print large images without losing quality.

In the pipeline options, you’ll find an argument called guidance_scale. This controls how much adherence the model will have to the text prompt during generation. A larger value will force it to better match the prompt, potentially at the cost of overall sample quality.

Another optional parameter is num_inference_steps, which is the number of steps Stable Diffusion will take to generate an image. More steps will yield higher-quality images, but they can also take longer to generate. You can experiment with this by changing the value, and if you’re satisfied with the results, you can start using your new image generator!

Applications

Stable diffusion can be used to generate images with many different styles. For example, it can be used to create a new artistic style by blending the characteristics of several artists together. It can also be used to emulate the style of a particular artist. This can be done by creating a model that learns from the output of other models, and then produces an image that matches that artist’s style. There are a few different ways to do this, but the key is to train the model correctly.

To do this, it is important to use a large dataset that includes lots of different variations of the same object or theme. This will help the model to learn the general shape and features of that object or theme, making it more versatile. In addition, it is important to train the model on a wide range of images to ensure that it has coverage of all possible combinations.

The best way to do this is to use a web application that allows you to upload your own training data and create an embedding file. Some examples include Dreambooth and Dall-E 2. These web applications offer free credits to users, but charge a fee once the user’s credit is depleted.

When using a web app to create an embedding, it is important to remember that latent diffusion models work in low-dimensional space, which significantly reduces memory and compute requirements compared to pixel-space models. As a result, the model can be trained to be very fast, even on 16GB Colab GPUs.

During training, the text prompts are first tokenized by a CLIP tokenizer. Then the model is generated using an autoencoder which converts the text to an image in latent space. This reduces the amount of memory required by a factor of 8.

If you want to generate images that match a specific topic, you can fine-tune the model by adding a hypernetwork layer. This adds a search function to the model that can return images matching the keywords given by the user. For example, if you want the model to find images of your dog, you can provide 5-8 high-quality pictures of your dog with various backgrounds, poses, and expressions.