Stable Diffusion Fine-Tuning Guide
If you’re interested in artificial intelligence (AI) art generation, then you’ll find Stable Diffusion quite useful in your image generation work. This is the latest AI art generation model developed by CompVis Group in conjunction with other software development companies and communities like Stability.ai.
Before you start using a Stable Diffusion AI generator for your artwork, you need to understand what it is, how it works, and how you can fine-tune it to make it more effective. Whether you’re using Stable Diffusion to create game assets or pure art pieces, the quality of your work will depend on your ability to use the AI art generator effectively.
What Is Stable Diffusion?
As noted above, Stable Diffusion is a text-to-image AI image generation program that generates realistic images through deep learning techniques. This program was developed and released in August 2022 by CompVis Group, Stability.ai, Runway, and LMU Munich. As a text-to-image model, Stable Diffusion generates images based on text descriptions.
Nevertheless, it can be applied to many other functions, including inpainting, outpainting, and image-to-image translations. But even these tasks should be guided by text prompts. Prompts are the sets of words that you give to the AI image generator to let it know the kind of image you need. When you’re creating a Stable Diffusion prompt, make sure it’s properly structured and detailed enough to guide the AI image generator to produce a perfect image.
Furthermore, you should ensure that your prompts are within the ideal Stable Diffusion prompt length. In other words, your prompts shouldn’t be too short or too long. Each prompt should be at least three words long so that it provides the AI with enough details to understand the kind of image you need. Also, it shouldn’t exceed 60 words. If your text prompts are too long, they’ll confuse the AI art generator and make the final image appear cluttered and unrealistic.
The Complete Fine-Tuning Guide
Fine-tuning your Stable Diffusion program requires three key elements: hardware, image-text pairs, and a pre-trained Stable Diffusion model. The initial implementation needs a lot of GPU resources to train the model to generate high-quality, realistic images. This implementation process may prove to be a bit complex for novice machine learning specialists, but it becomes easier as you go.
Therefore, you’ll need a computer with at least sixteen gigabytes (GB) RAM GPU to implement your Stable Diffusion model. Secondly, you’ll need a lot of images to train your model. Because this model tends to overfit training images, make sure your training subset images have the subject in various poses and positions.
While the original Stable Diffusion implementation guide recommends using between four and six images, you can use as many training images as possible. Since this is a deep-learning model, it’ll rely on the subsets you use to train it to generate your preferred images. Therefore, the more training images you use the better your final results.
Ensure that at least two training images in your subsets have the subject’s torso, and at least six images have the subject’s face in various positions, and with different backgrounds, expressions, and styles. Your training images shouldn’t be too large or too small. Therefore, you’ll need to crop them to the right square ratio of 64 x 64.
The other step in fine-tuning your Stable Diffusion model is to get the pre-trained model’s weights. You can download them from various online sources, and make sure to download them automatically because the training script does it automatically.
When you’re training your Stable Diffusion model, you need to define the parameters for the training process. The first parameter is the token name, which corresponds to a unique identifier that references the subject you wish to add. Choose a unique token name to avoid competition with the existing representations.
Secondly, choose a class name like a man, woman, dog, cat, etc. The third parameter is the number of regularisation images you intend to use. Finally, define your training iterations. The number of iterations shouldn’t be too low or too high because the model might overfit or underfit the subject’s image, thus reproducing inaccurate images during inference.