DiffMorph: Text-less Image Morphing with Diffusion Models
Shounak Chatterjee
Text-conditioned image generation models are a prevalent use of AI image
synthesis, yet intuitively controlling output guided by an artist remains
challenging. Current methods require multiple images and textual prompts for
each object to specify them as concepts to generate a single customized image.
  On the other hand, our work, \verb|DiffMorph|, introduces a novel approach
that synthesizes images that mix concepts without the use of textual prompts.
Our work integrates a sketch-to-image module to incorporate user sketches as
input. \verb|DiffMorph| takes an initial image with conditioning artist-drawn
sketches to generate a morphed image.
  We employ a pre-trained text-to-image diffusion model and fine-tune it to
reconstruct each image faithfully. We seamlessly merge images and concepts from
sketches into a cohesive composition. The image generation capability of our
work is demonstrated through our results and a comparison of these with
prompt-based image generation.