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..
DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models
Authors: Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, Jian Zhang
ICLR 2024 | Venue PDF | 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. |