Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
PostEdit: Posterior Sampling for Efficient Zero-Shot Image Editing
Authors: Feng Tian, Yixuan Li, Yichao Yan, Shanyan Guan, Yanhao Ge, Xiaokang Yang
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results indicate that the proposed Post Edit achieves state-of-the-art editing performance while accurately preserving unedited regions. Furthermore, the method is both inversion- and training-free, necessitating approximately 1.5 seconds and 18 GB of GPU memory to generate high-quality results. |
| Researcher Affiliation | Collaboration | 1Mo E Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University 2vivo Mobile Communication Co., Ltd EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Posterior Sampling for Image Editing |
| Open Source Code | Yes | Code: https://github.com/TFNTF/Post Edit. |
| Open Datasets | Yes | To ensure a fair comparison, all experiments were conducted on the PIE-Bench dataset Ju et al. (2024) using the same parameter settings specified in Appendix A.2 and a single A100 GPU to evaluate both image quality and inference efficiency. The PIE-Bench dataset comprises 700 images with 10 types of editing, where each image is paired with a source prompt and a target prompt. |
| Dataset Splits | Yes | The PIE-Bench dataset comprises 700 images with 10 types of editing, where each image is paired with a source prompt and a target prompt. In our experiments, the resolution of all test images was set to 512 Ć 512. For the reconstruction experiments, we set the initial and target prompts to be identical across all test runs. |
| Hardware Specification | Yes | Additionally, the method is both inversion- and training-free, necessitating approximately 1.5 seconds and 18 GB of GPU memory to generate high-quality results. |
| Software Dependencies | No | The paper mentions specific models and solvers like 'LCM-SD1.5' and 'Dreamshaper v7 fine-tune of Stable-Diffusion v1-5' but does not provide version numbers for general software libraries such as Python, PyTorch, or CUDA, which are typically required for replication. |
| Experiment Setup | Yes | The main hyper-parameters of the Post Edit are briefly summarized in Tab. 3. Parameters of Consistency models. cskip and cout shown in line 6 of Alg. 1 are set to 0 and 1 for most cases respectively. Hyper-parameters in Alg. 1. N is set to 5 for schedule {Ļi}Nā1i=0 . To ensure higher efficiency and quality at the same time, zN is sampled through zN ā¼ N Ī±t z0, 1 ā Ī±t I , (18) where t is set to 501 generally following the DDPM scheduler Ho et al. (2020). |