7 Conditional Image Generation
This chapter covers
- The limitations of unconditional generative models and the need for conditional image generation.
- Applications of conditional image generation, including text-to-image synthesis, style transfer, and image-to-image translation.
- Types of conditioning input into generative models, such as class labels, other images, and textual descriptions.
- Implementation of Conditional Variational Autoencoders (cVAEs) for controlled image generation using class labels.
Generative models like VAEs, GANs, and Diffusion Models learn to capture the underlying data distribution and generate images that are statistically similar to the training data. However, the models we discussed and implemented so far were unconditional, meaning they generate images without any specific guidance or control over the output. While unconditional generative models have demonstrated impressive results in producing realistic images, they suffer from a significant limitation: lack of control over the generated content.
Conditional image generation addresses this limitation by incorporating additional information into the generative process, allowing for more controlled and purposeful image synthesis. By conditioning on various types of information — such as class labels, other images, or textual descriptions — we can guide the model to generate images that meet specific criteria or exhibit certain attributes.