Analogy: You want to draw a detailed dragon, but starting with a complex drawing can be overwhelming. So, we first draw a simple version of the dragon. This simple sketch captures the basic shape and important features of the dragon but is not very detailed.
Now, we start adding random scribbles and doodles to our simple dragon sketch. These scribbles make the sketch look messy and less recognizable as a dragon.
To create a new dragon sketch, we start with the messy, scribbled version again.
We use an eraser to slowly remove the scribbles. Each time we erase, we carefully try to make the sketch look more like a dragon again.
With each step of erasing, the dragon becomes clearer and more detailed, until it resembles a clean, simple dragon sketch again.
Once we have a clean and clear simple sketch of the dragon, we use it as a guide to create a big, detailed drawing.
We add scales, wings, fire, and all the other intricate details that make the dragon look amazing.
This detailed drawing is based on the cleaned-up simple sketch, ensuring that it looks good and has all the important features.
This process of starting with a simple version, adding noise (scribbles), and then cleaning it up step by step to create a detailed version is similar to how a latent diffusion model works in generating high-quality data, like images or sounds. It simplifies the task and ensures great results by focusing on the essential features first and refining them gradually.
What’s the advantage of Stable Diffusion?
It is open-source, has many free tools and models
It is designed for Lowe-power computers
What is inpainting?
Inpainting is used to regenerate part of an AI or real image.
What is CFG Scale?
Creative Freedom Guidance Scale, ranges from 1-30
1 being most creative and ignore our prompt mostly
30 being most restrictive and adheres to the prompt as closely as possible
By adjusting the figure, we control the balance between creativity and order
What are sampling methods?
Sampling methods are the techniques used by AI models to create new images from noise. Think of it like baking cookies from raw ingredients. Different methods mix and bake these ingredients (data points) in various ways to get the final image (cookies).
Euler (Classic Recipe):
How It Works: This is like a traditional cookie recipe. You follow simple, step-by-step instructions. Each step adds a bit more detail to the image.
Characteristics: It’s straightforward and reliable, like following a classic cookie recipe that everyone knows. You get good results most of the time, but it might not always capture the finest details perfectly.
LMS (Low-Memory Mixing):
How It Works: This method is like a recipe for making cookies with fewer ingredients and steps, but still aiming for tasty results. It’s designed to be efficient and use less memory.
Characteristics: It’s fast and doesn’t require a lot of resources. Think of it as making cookies quickly with fewer tools. It might not always be as detailed as other methods, but it gets the job done efficiently.
DPM (Diffusion Process Mixing):
How It Works: This is like a fancy, high-tech cookie recipe that uses advanced tools and techniques to mix the ingredients. It carefully controls the process to make sure every detail is perfect.
Characteristics: It’s very precise and can create highly detailed and realistic images, like making gourmet cookies with the perfect texture and flavor. However, it might take longer and require more resources.
