How to Evaluate with DragBench

Step 1: extract dataset

Extract DragBench into the folder "drag_bench_data". Resulting directory hierarchy should look like the following:

drag_bench_data
--- animals
------ JH_2023-09-14-1820-16
------ JH_2023-09-14-1821-23
------ JH_2023-09-14-1821-58
------ ...
--- art_work
--- building_city_view
--- ...
--- other_objects

Step 2: train LoRA.

Train one LoRA on each image in drag_bench_data. To do this, simply execute "run_lora_training.py". Trained LoRAs will be saved in "drag_bench_lora"

Step 3: run dragging results

To run dragging results of DragDiffusion on images in "drag_bench_data", simply execute "run_drag_diffusion.py". Results will be saved in "drag_diffusion_res".

Step 4: evaluate mean distance and similarity.

To evaluate LPIPS score before and after dragging, execute "run_eval_similarity.py" To evaluate mean distance between target points and the final position of handle points (estimated by DIFT), execute "run_eval_point_matching.py"

Expand the Dataset

Here we also provided the labeling tool used by us in the file "labeling_tool.py". Run this file to get the user interface for labeling your images with drag instructions.