Method Overview
Overview of iMontage. The model accepts a flexible set of reference images and produces N outputs conditioned on a text prompt. Images are encoded by a 3D VAE separately, text by a language model, and both token streams are processed by an MMDiT. We concatenate clean reference-image tokens with noisy target tokens before denoising. Right: training uses fixed-length text tokens and variable-length image/noise tokens, transitions from dual stream to single stream blocks. For image branch, we apply Marginal RoPE, a head-tail temporal indexing that separates input and output pseudo-frames, preserves spatial RoPE, and supports many-to-many generation. In figure, notation H and W with subscription denote the height/width indices of the 2D RoPE computed at the images' native resolution, while notation T represents assigned time index for temporal dimension.
