Object-Aware Inversion and Reassembly for Image Editing
Authors: Zhen Yang, Ganggui Ding, Wen Wang, Hao Chen, Bohan Zhuang, Chunhua Shen
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our method achieves superior performance in editing object shapes, colors, materials, categories, etc., especially in multi-object editing scenarios. |
| Researcher Affiliation | Academia | 1 Zhejiang University, China {zheny.cs,dingangui,wwenxyz,haochen.cad,chunhuashen}@zju.edu.cn 2 Monash University, Australia bohan.zhuang@monash.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The project page can be found here. |
| Open Datasets | No | To systematically evaluate the proposed method, we collect two new datasets containing 208 and 100 single- and multi-object text-image pairs, respectively. Both quantitative and qualitative experiments demonstrate that our method achieves competitive performance in single-object editing, and outperforms state-of-the-art (SOTA) methods by a large margin in multi-object editing scenarios. |
| Dataset Splits | No | No specific training, validation, or test dataset splits (percentages, counts, or predefined splits) were explicitly provided in the paper. |
| Hardware Specification | Yes | All our experiments are conducted on the Ge Force RTX 3090. |
| Software Dependencies | No | We use Diffusers5 implementation of Stable Diffusion v1.4 6 in our experiments. ... We employ the CLIP base model ... and use Grounded-SAM7 to generate masks. |
| Experiment Setup | Yes | For DDIM Inversion, we used a uniform setting of 50 steps. ... The random seed is set to 1 for all experiments. ... In our experiments, the re-inversion step ire is also set to 20% of the total inversion steps, as we empirically found that it performs well for most situations. |