  Diffusion-model-based text-guided image generation has recently made
astounding progress, producing fascinating results in open-domain image
manipulation tasks. Few models, however, currently have complete zero-shot
capabilities for both global and local image editing due to the complexity and
diversity of image manipulation tasks. In this work, we propose a method with a
mixture-of-expert (MOE) controllers to align the text-guided capacity of
diffusion models with different kinds of human instructions, enabling our model
to handle various open-domain image manipulation tasks with natural language
instructions. First, we use large language models (ChatGPT) and conditional
image synthesis models (ControlNet) to generate a large number of global image
transfer dataset in addition to the instruction-based local image editing
dataset. Then, using an MOE technique and task-specific adaptation training on
a large-scale dataset, our conditional diffusion model can edit images globally
and locally. Extensive experiments demonstrate that our approach performs
surprisingly well on various image manipulation tasks when dealing with
open-domain images and arbitrary human instructions. Please refer to our
project page: [https://oppo-mente-lab.github.io/moe_controller/]
