Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent
  Diffusion Models for Virtual Try-All
Mehmet Saygin Seyfioglu Karim Bouyarmane Suren Kumar Amir Tavanaei Ismail B. Tutar
As online shopping is growing, the ability for buyers to virtually visualize
products in their settings-a phenomenon we define as "Virtual Try-All"-has
become crucial. Recent diffusion models inherently contain a world model,
rendering them suitable for this task within an inpainting context. However,
traditional image-conditioned diffusion models often fail to capture the
fine-grained details of products. In contrast, personalization-driven models
such as DreamPaint are good at preserving the item's details but they are not
optimized for real-time applications. We present "Diffuse to Choose," a novel
diffusion-based image-conditioned inpainting model that efficiently balances
fast inference with the retention of high-fidelity details in a given reference
item while ensuring accurate semantic manipulations in the given scene content.
Our approach is based on incorporating fine-grained features from the reference
image directly into the latent feature maps of the main diffusion model,
alongside with a perceptual loss to further preserve the reference item's
details. We conduct extensive testing on both in-house and publicly available
datasets, and show that Diffuse to Choose is superior to existing zero-shot
diffusion inpainting methods as well as few-shot diffusion personalization
algorithms like DreamPaint.