FiVA Dataset

Project page | Paper

News

• [2024-12] We will be uploading version 2 during the first week of December (data/full_data_v2). This update includes additional attribute definitions and a more stringent filtering process.
• [2024-08] The first version of the FiVA dataset has been released.

Data structure

Folder ./data/data_part0 contains an example subset and folder ./data/full_data contains the full data currently, 1.04M images in total. Under each .zip package, data is arranged as below:

- package_idx
--- package_idx.json # metadata
--- images
----- 00001.png
----- 00002.png
...

Each sample is a 2x2 image grid at the resolution of 1024x1024, we count the data sample as ONE sample, allowing for more space and diversity for randomly choosing different sample within the 4 images at training time.

metadata.json contains the metadata for each sample, where we show an example as below:

# Metadata structure
[
    {
      "idx": ..., # index
      "image_path": "", # path to the image
      "features": {
            "attributes": [{"attribute type": "..."}, {"attribute type": "..."}], # attribute types and the specific description in this sample.
            "subject": "..." # subject name
        },
      "prompt": "...", # the prompt used for image generation
    },
  ...
]

# An example
[
    {
      "idx": 0,
      "image_path": "0/00000.png", 
      "features": {
            "attributes": [
                {
                    "lighting": "1@hard light, highlighting texture, sharp shadow"
                },
                {
                    "color": "30@soft lavender, mint green, pale peach, and baby blue"
                }
            ],
            "subject": "introspective sports portrait"
        },
       "prompt": "soft lavender, mint green, pale peach, and baby blue, hard light, highlighting texture, sharp shadow, introspective sports portrait",
    },
  ...
]

Code and supporting files

Attributes and Subjects

./code/attributes_and_subjects.json contains the attribute and subject dictionaries.

Range-sensitive filtering

./code/range_sensitive_filter.json contains our meta data for the filter, and ./code/data_filter.py converts it into a format that can be used in the dataloader.

Data Loader

./code/dataloader.py provides an example in loading the data into image pairs with the filter and balanced resampling adopted.