This Computerphile video explains how AI image generators, such as Stable Diffusion and Dall-E, function. It contrasts these newer diffusion models with older generative adversarial networks (GANs) and details the iterative process of noise reduction used to create images from random noise, eventually guided by text prompts.
Generative Adversarial Networks (GANs): The video begins by explaining GANs, a previous standard for generating images. GANs involve a generator network producing images and a discriminator network evaluating their realism. However, GANs are challenging to train and prone to mode collapse (producing repetitive outputs).
Diffusion Models: The core of the video focuses on diffusion models as a more efficient alternative. These models iteratively remove noise from a completely random image, gradually refining it into a coherent picture. This process is more stable and easier to train than GANs.
Noise Schedule: Diffusion models utilize a "noise schedule," which dictates how much noise is added at each step. Linear (constant noise addition) and non-linear schedules are discussed.
Text-Guided Image Generation: The video describes how text prompts are incorporated to guide image generation. Text embeddings, created using transformer models similar to GPT, are fed into the network to direct the noise reduction process toward a desired outcome. Classifier-free guidance, a technique using two network passes (one with, one without text embedding), enhances the accuracy of text-guided generation.
Accessibility: The video mentions that Stable Diffusion, unlike some other models, is available for free use, making it accessible to individuals via platforms like Google Colab.
AI image generators like Stable Diffusion and Dall-E use diffusion models, iteratively removing noise from random data to create images. This is more efficient and stable than older GAN methods. Text prompts guide the process via text embeddings, and a technique called classifier-free guidance further refines results. Stable Diffusion offers free access via platforms like Google Colab.
