From the course: Hands-On Generative AI with Diffusion Models: Building Real-World Applications
Unconditional image generation with diffusion models
From the course: Hands-On Generative AI with Diffusion Models: Building Real-World Applications
Unconditional image generation with diffusion models
- Let's take a look at the simplest use-case of diffusion models, which is unconditional image generation. Before we dive right in, let's also take a moment to understand how did we get here. The evolution of image generation models started with the generative adversarial networks, variational autoencoders, and flow-based models. These models have significantly advanced image generation over the past decade. While known for their high quality outputs, these models have struggled with training diversity and stability due to their adversarial design. This is where diffusion models come in. This is a novel approach grounded in non-equilibrium thermodynamics. These models utilize a Markov chain process to incrementally add noise to data, then masterfully reverse this diffusion process, enabling the reconstruction of new images from noise. So let's take a look at how this process works through unconditional image generation. Unconditional image generation represents a paradigm shift in model inference for image generation. These models are exposed to an array of images during their training process, and they learn to emulate these patterns when we ask them to generate a new image from a noise vector. A noise vector is supplied as an input, and the stable diffusion model simply generates an output image without any existing context. This context can be anything from text and images. The unique aspect here is the independent generation process free from specific external inputs. They are leveraging the inherent knowledge they acquire during training. The creative process in this domain begins with a seed, a randomly generated noise vector. The seed is the foundation from which the model starts its creative journey, leading to the generation of unique and varied images, each distinctively influenced by the initial randomness of the seed. So where is this used? Unconditional image generation is revolutionizing multiple sectors. It's forging new paths in artistic creativity, augmenting AI training data for enhanced robustness, crafting more realistic environments in virtual reality, and contributing to medical imaging advancements. These applications underscore the technologies, adaptability, and far reaching impact across diverse fields.
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