ACE: All-round Creator and Editor Following Instructions via Diffusion
  Transformer
Authors: name: Zhen Han, name: Zeyinzi Jiang, name: Yulin Pan, name: Jingfeng Zhang, name: Chaojie Mao, name: Chenwei Xie, name: Yu Liu, name: Jingren Zhou

Diffusion models have emerged as a powerful generative technology and have
been found to be applicable in various scenarios. Most existing foundational
diffusion models are primarily designed for text-guided visual generation and
do not support multi-modal conditions, which are essential for many visual
editing tasks. This limitation prevents these foundational diffusion models
from serving as a unified model in the field of visual generation, like GPT-4
in the natural language processing field. In this work, we propose ACE, an
All-round Creator and Editor, which achieves comparable performance compared to
those expert models in a wide range of visual generation tasks. To achieve this
goal, we first introduce a unified condition format termed Long-context
Condition Unit (LCU), and propose a novel Transformer-based diffusion model
that uses LCU as input, aiming for joint training across various generation and
editing tasks. Furthermore, we propose an efficient data collection approach to
address the issue of the absence of available training data. It involves
acquiring pairwise images with synthesis-based or clustering-based pipelines
and supplying these pairs with accurate textual instructions by leveraging a
fine-tuned multi-modal large language model. To comprehensively evaluate the
performance of our model, we establish a benchmark of manually annotated pairs
data across a variety of visual generation tasks. The extensive experimental
results demonstrate the superiority of our model in visual generation fields.
Thanks to the all-in-one capabilities of our model, we can easily build a
multi-modal chat system that responds to any interactive request for image
creation using a single model to serve as the backend, avoiding the cumbersome
pipeline typically employed in visual agents. Code and models will be available
on the project page: https://ali-vilab.github.io/ace-page/.