  Fashionable image generation aims to synthesize images of diverse fashion
prevalent around the globe, helping fashion designers in real-time
visualization by giving them a basic customized structure of how a specific
design preference would look in real life and what further improvements can be
made for enhanced customer satisfaction. Moreover, users can alone interact and
generate fashionable images by just giving a few simple prompts. Recently,
diffusion models have gained popularity as generative models owing to their
flexibility and generation of realistic images from Gaussian noise. Latent
diffusion models are a type of generative model that use diffusion processes to
model the generation of complex data, such as images, audio, or text. They are
called "latent" because they learn a hidden representation, or latent variable,
of the data that captures its underlying structure. We propose a method
exploiting the equivalence between diffusion models and energy-based models
(EBMs) and suggesting ways to compose multiple probability distributions. We
describe a pipeline on how our method can be used specifically for new
fashionable outfit generation and virtual try-on using LLM-guided text-to-image
generation. Our results indicate that using an LLM to refine the prompts to the
latent diffusion model assists in generating globally creative and culturally
diversified fashion styles and reducing bias.
