Intro to Latent Diffusion Models - Stable Diffusion Masterclass

Key Takeaways

welcome back to the Steeples recourse on diffusion models I'm Mandy and in this lesson we'll be getting introduced to latent diffusion models latent diffusion models are generative deep learning models that have skyrocketed in popularity not only amongst the AI Community but out in the general public as well let's now begin our journey into investigating what exactly a latent diffusion model is starting with its precursor the diffusion model at the highest level a diffusion model is a generative deep learning model as we know deep learning models make use of deep neural networks and a generative model is a model that is used to generate new data so at a really high level we can think of a diffusion model just as a deep neural network that generates things and as we are likely now familiar with these things that we are currently most interested in using diffusion models for are images now let's take it a step further to understand the actual name diffusion in general diffusion models are trained to denoise data in a sequential step-by-step fashion as we'll later become familiar with this denoising process is how a diffusion model can actually generate images for now we can just think about the training data for diffusion models consisting of noisy images and we can think about these diffusion models as being trained to remove the noise from the noisy training data so that we're left with these clear image samples and the way in which the diffusion model removes the noise from the training images is generally referred to as the diffusion process later we'll develop an understanding for how exactly this noise removal diffusion process results in the cool images that we've seen these models generate for now though we can just think of these models as accepting noisy training data and outputting clear images from them now elaborating more on the nomenclature here we've probably all at this point at least heard of really popular models like stable diffusion and stable diffusion is indeed a latent diffusion model now we haven't yet said what a latent diffusion model is we've been harping on diffusion models as we can see that latent diffusion models are a subset of diffusion models and of course as we alluded to earlier diffusion models are generative deep learning models so now let's zoom in on the idea of latent diffusion models to see how they differ from diffusion models in general recall that we refer to the diffusion process as this denoising process that diffusion models do to remove noise from noisy training data now General diffusion models apply this denoising diffusion process on the pixel space of the image data and as we know images can be very large in size and training on image data can require a lot of computational resources so here is where latent diffusion models come into play latent diffusion models actually work with compressed representations of the original image data and these compressed represent notations are referred to as latents so in other words latent diffusion models work in lower dimensional latent space rather than a higher dimensional pixel space which reduces the amount of computational resources and therefore improves efficiency so this is one of the reasons that models like stable diffusion are so impressive not only can they generate these amazing and compelling Creations that we've all seen but they also don't need much computational resources at least relatively in order to do so now that we've been introduced to the concept of latent diffusion models in general we're ready to begin at dissecting these models and investigating their various components and training mechanisms that allow these models to work