DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models
Authors: Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, Jian Zhang
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method has promising performance on various image editing tasks, including within a single image (e.g., object moving, resizing, and content dragging) or across images (e.g., appearance replacing and object pasting). |
| Researcher Affiliation | Collaboration | 1School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University 2ARC Lab, Tencent PCG 3Peking University Shenzhen Graduate School-Rabbitpre AIGC Joint Research Laboratory |
| Pseudocode | No | The paper describes the method and its components but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/MC-E/Dragon Diffusion. |
| Open Datasets | Yes | The test set is randomly formed by 800 aligned faces from Celeb A-HQ Karras et al. (2018) training set. |
| Dataset Splits | No | The paper mentions using a 'test set' formed from the 'Celeb A-HQ training set' but does not provide specific dataset split information (percentages, sample counts, or explicit validation split details) needed to reproduce the data partitioning for their experiments. |
| Hardware Specification | Yes | The experiment is conducted on an NVIDIA A100 GPU with Float32 precision. |
| Software Dependencies | No | The paper mentions using 'Stable Diffusion-V1.5' and 'DDIM sampling' but does not provide specific version numbers for software dependencies such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | The inference adopts DDIM sampling with 50 steps, and we set the classifier-free guidance scale as 5. |