StereoDiffusion: Training-Free Stereo Image Generation Using Latent
  Diffusion Models
Lezhong Wang Jeppe Revall Frisvad Mark Bo Jensen Siavash Arjomand Bigdeli
The demand for stereo images increases as manufacturers launch more XR
devices. To meet this demand, we introduce StereoDiffusion, a method that,
unlike traditional inpainting pipelines, is trainning free, remarkably
straightforward to use, and it seamlessly integrates into the original Stable
Diffusion model. Our method modifies the latent variable to provide an
end-to-end, lightweight capability for fast generation of stereo image pairs,
without the need for fine-tuning model weights or any post-processing of
images. Using the original input to generate a left image and estimate a
disparity map for it, we generate the latent vector for the right image through
Stereo Pixel Shift operations, complemented by Symmetric Pixel Shift Masking
Denoise and Self-Attention Layers Modification methods to align the right-side
image with the left-side image. Moreover, our proposed method maintains a high
standard of image quality throughout the stereo generation process, achieving
state-of-the-art scores in various quantitative evaluations.