  Current text-to-image editing models often encounter challenges with smoothly
manipulating multiple attributes using a single instruction. Taking inspiration
from the Chain-of-Thought prompting technique utilized in language models, we
present an innovative concept known as Chain-of-Instruct Editing (CoIE), which
enhances the capabilities of these models through step-by-step editing using a
series of instructions. In particular, in the context of face manipulation, we
leverage the contextual learning abilities of a pretrained Large Language Model
(LLM), such as GPT-4, to generate a sequence of instructions from the original
input, utilizing a purpose-designed 1-shot template. To further improve the
precision of each editing step, we conduct fine-tuning on the editing models
using our self-constructed instruction-guided face editing dataset,
Instruct-CelebA. And additionally, we incorporate a super-resolution module to
mitigate the adverse effects of editability and quality degradation.
Experimental results across various challenging cases confirm the significant
boost in multi-attribute facial image manipulation using chain-of-instruct
editing. This is evident in enhanced editing success rates, measured by CLIPSim
and Coverage metrics, improved by 17.86% and 85.45% respectively, and
heightened controllability indicated by Preserve L1 and Quality metrics,
improved by 11.58% and 4.93% respectively.
