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 [1].
FastDrag: Manipulate Anything in One Step
Authors: Xuanjia Zhao, Jian Guan, Congyi Fan, Dongli Xu, Youtian Lin, Haiwei Pan, Pengming Feng
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our Fast Drag is validated on the Drag Bench dataset, demonstrating substantial improvements in processing time over existing methods, while achieving enhanced editing performance. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Harbin Engineering University 2School of Intelligence Science and Technology, Nanjing University 3State Key Laboratory of Space-Ground Integrated Information Technology 4Independent Researcher |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project page: https://fastdrag-site.github.io/. We include the implementation code for our method in the supplementary materials submitted. |
| Open Datasets | Yes | We conduct quantitative comparison using Drag Bench dataset [28], which consists of 205 different types of images with 349 pairs of handle and target points. |
| Dataset Splits | No | The paper uses the Drag Bench dataset for quantitative comparison and ablation studies but does not specify any training, validation, or test splits for its experiments. |
| Hardware Specification | Yes | Our experiments are conducted on an RTX 3090 GPU with 24G memory. |
| Software Dependencies | No | The paper mentions 'Stable Diffusion 1.5' but does not provide specific version numbers for programming languages or libraries like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | Unless otherwise specified, the default setting for inversion and sampling step is 10. Following Drag Diffusion [28], classifier-free guidance (CFG) [8] is not applied in diffusion model, and we optimize the diffusion latent at the 7th step. |