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IterDiff (ICIP 2025)

IterDiff: Training-Free Iterative Face Editing via Efficient CLIP-guided Memory Bank

Chun-Yao Chiu1, Feng-Kai Huang2, Teng-Fang Hsiao1, Hong-Han Shuai1, Wen-Huang Cheng2

1Institute of Electrical and Computer Engineering, National Yang Ming Chiao Tung University 2Department of Computer Science and Information Engineering, National Taiwan University

Project Overview

This repository provides inference and evaluation pipelines for IterDiff and related methods (ip2p, scfg, iterdiff, and emilie). It includes:

  • Multi-step face editing inference
  • Attention control and memory bank mechanisms
  • Quantitative evaluation with CLIP-I, LPIPS, and ImageReward

Environment Setup

Using Conda is recommended (environment.yml is provided, Python 3.12):

conda env create -f environment.yml
conda activate iterdiff

Data Preparation

This project uses the FFHQ download script from StyleGAN2.

cd datasets/ffhq
python download_ffhq.py --images

After downloading, make sure images are located at:

  • datasets/ffhq/images1024x1024/

Quick Start

  1. Run Inference

    Use script_eval.py to generate multi-step editing results.

    Example (IterDiff):

    python script_eval.py \
 --type iterdiff \
 --mb_size 30 \
 --mb_save_topk 20 \
 --exp_title exp_iterdiff \
 --iter_edit_bench iter_edit_bench_1000.json \
 --results_dir results

  2. Compute Metrics

    Use compute_metric.py: