The Geneval-style dataset is sourced from BLIP3o-60k.

This dataset is presented in the paper: UniWorld: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation

More details can be found in UniWorld-V1

Data preparation

Download the data from LanguageBind/UniWorld-V1. The dataset consists of two parts: source images and annotation JSON files.

Prepare a data.txt file in the following format:

1. The first column is the root path to the image.

2. The second column is the corresponding annotation JSON file.

3. The third column indicates whether to enable the region-weighting strategy. We recommend setting it to True for edited data and False for others.

data/BLIP3o-60k,json/blip3o_t2i_58859.json,false
data/coco2017_caption_canny-236k,coco2017_canny_236574.json,false
data/imgedit,json/imgedit/laion_add_part0_edit.json,true

We have prepared a data.txt file about ImgEdit for your reference.

data/imgedit/action/action,json/imgedit/pandam_action_edit.json,true
data/imgedit/action/action_part2,json/imgedit/pandam2_action_edit.json,true
data/imgedit/action/action_part3,json/imgedit/pandam3_action_edit.json,true
data/imgedit/action/action_part4,json/imgedit/pandam4_action_edit.json,true
data/imgedit/add/add_part0,json/imgedit/laion_add_part0_edit.json,true
data/imgedit/add/add_part1,json/imgedit/laion_add_part1_edit.json,true
data/imgedit/add/add_part4,json/imgedit/results_add_laion_part4_edit.json,true
data/imgedit/add/add_part5,json/imgedit/results_add_laion_part5_edit.json,true
data/imgedit/adjust/adjust_part0,json/imgedit/results_adjust_canny_laion_part0_edit.json,true
data/imgedit/adjust/adjust_part2,json/imgedit/results_adjust_canny_laion_part2_edit.json,true
data/imgedit/adjust/adjust_part3,json/imgedit/results_adjust_canny_laion_part3_edit.json,true
data/imgedit/adjust/adjust_part4,json/imgedit/laion_adjust_canny_part4_edit.json,true
data/imgedit/background/background_part0,json/imgedit/results_background_laion_part0_edit.json,true
data/imgedit/background/background_part2,json/imgedit/results_background_laion_part2_edit.json,true
data/imgedit/background/background_part3,json/imgedit/laion_background_part3_edit.json,true
data/imgedit/background/background_part5,json/imgedit/laion_background_part5_edit.json,true
data/imgedit/background/background_part7,json/imgedit/laion_background_part7_edit.json,true
data/imgedit/compose/compose_part0,json/imgedit/results_compose_part0_edit.json,false
data/imgedit/compose/compose_part2,json/imgedit/results_compose_part2_edit.json,false
data/imgedit/compose/compose_part6,json/imgedit/results_compose_part6_fix_edit.json,false
data/imgedit/refine_replace/refine_replace_part1,json/imgedit/results_extract_ref_part1_refimg_edit.json,true
data/imgedit/remove/remove_part0,json/imgedit/laion_remove_part0_edit.json,true
data/imgedit/remove/remove_part1,json/imgedit/results_remove_laion_part1_edit.json,true
data/imgedit/remove/remove_part4,json/imgedit/results_remove_laion_part4_edit.json,true
data/imgedit/remove/remove_part5,json/imgedit/results_remove_laion_part5_edit.json,true
data/imgedit/replace/replace_part0,json/imgedit/laion_replace_part0_edit.json,true
data/imgedit/replace/replace_part1,json/imgedit/laion_replace_part1_edit.json,true
data/imgedit/replace/replace_part4,json/imgedit/results_replace_laion_part4_edit.json,true
data/imgedit/replace/replace_part5,json/imgedit/results_replace_laion_part5_edit.json,true
data/imgedit/transfer/transfer,json/imgedit/results_style_transfer_edit.json,false
data/imgedit/transfer/transfer_part0,json/imgedit/results_style_transfer_part0_cap36472_edit.json,false

Data details

Text-to-Image Generation

• BLIP3o-60k: We add text-to-image instructions to half of the data. [108 GB storage usage.]
• OSP1024-286k: Sourced from internal data of the Open-Sora Plan, with captions generated using Qwen2-VL-72B. Images have an aspect ratio between 3:4 and 4:3, aesthetic score ≥ 6, and a short side ≥ 1024 pixels. [326 GB storage usage.]

Image Editing

• imgedit-724k: Data is filtered using GPT-4o, retaining approximately half. [2.8T storage usage.]
• OmniEdit-368k: For image editing data, samples with edited regions smaller than 1/100 were filtered out; images have a short side ≥ 1024 pixels. [204 GB storage usage.]
• SEED-Data-Edit-Part1-Openimages-65k: For image editing data, samples with edited regions smaller than 1/100 were filtered out. Images have a short side ≥ 1024 pixels. [10 GB storage usage.]
• SEED-Data-Edit-Part2-3-12k: For image editing data, samples with edited regions smaller than 1/100 were filtered out. Images have a short side ≥ 1024 pixels. [10 GB storage usage.]
• PromptfixData-18k: For image restoration data and some editing data, samples with edited regions smaller than 1/100 were filtered out. Images have a short side ≥ 1024 pixels. [9 GB storage usage.]
• StyleBooth-11k: For transfer style data, images have a short side ≥ 1024 pixels. [4 GB storage usage.]
• Ghibli-36k: For transfer style data, images have a short side ≥ 1024 pixels. Warning: This data has not been quality filtered. [170 GB storage usage.]

Extract & Try-on

• viton_hd-23k: Converted from the source data into an instruction dataset for product extraction. [1 GB storage usage.]
• deepfashion-27k: Converted from the source data into an instruction dataset for product extraction. [1 GB storage usage.]
• shop_product-23k: Sourced from internal data of the Open-Sora Plan, focusing on product extraction and virtual try-on, with images having a short side ≥ 1024 pixels. [12 GB storage usage.]

Image Perception

• coco2017_caption_canny-236k: img->canny & canny->img [25 GB storage usage.]
• coco2017_caption_depth-236k: img->depth & depth->img [8 GB storage usage.]
• coco2017_caption_hed-236k: img->hed & hed->img [13 GB storage usage.]
• coco2017_caption_mlsd-236k: img->mlsd & mlsd->img [ GB storage usage.]
• coco2017_caption_normal-236k: img->normal & normal->img [10 GB storage usage.]
• coco2017_caption_openpose-62k: img->pose & pose->img [2 GB storage usage.]
• coco2017_caption_sketch-236k: img->sketch & sketch->img [15 GB storage usage.]
• unsplash_canny-20k: img->canny & canny->img [2 GB storage usage.]
• open_pose-40k: img->pose & pose->img [4 GB storage usage.]
• mscoco-controlnet-canny-less-colors-236k: img->canny & canny->img [13 GB storage usage.]
• coco2017_seg_box-448k: img->detection & img->segmentation (mask), instances with regions smaller than 1/100 were filtered out. We visualise masks on the original image as gt-image. [39 GB storage usage.]
• viton_hd-11k: img->pose [1 GB storage usage.]
• deepfashion-13k: img->pose [1 GB storage usage.]