  Multistep instructions, such as recipes and how-to guides, greatly benefit
from visual aids, such as a series of images that accompany the instruction
steps. While Large Language Models (LLMs) have become adept at generating
coherent textual steps, Large Vision/Language Models (LVLMs) are less capable
of generating accompanying image sequences. The most challenging aspect is that
each generated image needs to adhere to the relevant textual step instruction,
as well as be visually consistent with earlier images in the sequence. To
address this problem, we propose an approach for generating consistent image
sequences, which integrates a Latent Diffusion Model (LDM) with an LLM to
transform the sequence into a caption to maintain the semantic coherence of the
sequence. In addition, to maintain the visual coherence of the image sequence,
we introduce a copy mechanism to initialise reverse diffusion processes with a
latent vector iteration from a previously generated image from a relevant step.
Both strategies will condition the reverse diffusion process on the sequence of
instruction steps and tie the contents of the current image to previous
instruction steps and corresponding images. Experiments show that the proposed
approach is preferred by humans in 46.6% of the cases against 26.6% for the
second best method. In addition, automatic metrics showed that the proposed
method maintains semantic coherence and visual consistency across steps in both
domains.
