PnP Inversion: Boosting Diffusion-based Editing with 3 Lines of Code
Authors: Xuan Ju, Ailing Zeng, Yuxuan Bian, Shaoteng Liu, Qiang Xu
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To assess image editing performance, we present PIE-Bench, an editing benchmark with 700 images showcasing diverse scenes and editing types, accompanied by versatile annotations and comprehensive evaluation metrics. Compared to state-of-the-art optimization-based inversion techniques, our solution not only yields superior performance across 8 editing methods but also achieves nearly an order of speed-up. |
| Researcher Affiliation | Collaboration | Xuan Ju1,2 , Ailing Zeng2 , Yuxuan Bian1, Shaoteng Liu1, Qiang Xu1 1The Chinese University of Hong Kong (CUHK) 2International Digital Economy Academy (IDEA) |
| Pseudocode | Yes | Algorithm 1: Real Image Editing Pipeline with Pn P Inversion |
| Open Source Code | Yes | 2Code is available at https://github.com/cure-lab/Pn PInversion. |
| Open Datasets | No | No, the paper introduces PIE-Bench, a benchmark dataset of 700 images, and describes its construction in detail. However, it does not provide a direct URL, DOI, or specific repository name for accessing the dataset itself. |
| Dataset Splits | No | No, the paper introduces the PIE-Bench dataset and evaluates performance on it, but it does not provide specific details on how the dataset is split into training, validation, and test sets for model training or evaluation. |
| Hardware Specification | Yes | We perform the inference of different editing and inversion methods in the same setting unless specifically clarified, i.e., on RTX3090 following their open-source code with a base model of Stabe Diffusion v1.4 in 50 steps, with an inverse guidance scale of 1 and a forward guidance scale of 0. ... We test inference time per image of different inversion techniques and Prompt-to Prompt (Hertz et al., 2023) on one NVIDIA A800 80G to evaluate efficiency. |
| Software Dependencies | No | No, the paper mentions using 'Stable Diffusion v1.4' as a base model but does not provide specific version numbers for software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We perform the inference of different editing and inversion methods in the same setting unless specifically clarified, i.e., on RTX3090 following their open-source code with a base model of Stabe Diffusion v1.4 in 50 steps, with an inverse guidance scale of 1 and a forward guidance scale of 0. |