Update README.md

README.md

@@ -18,7 +18,7 @@ pretty_name: DiffusionDB-Pixelart
 size_categories:
 - n>1T
 source_datasets:
-- original
+- modified
 tags:
 - stable diffusion
 - prompt engineering
@@ -84,7 +84,6 @@ task_ids:
 - **Repository:** [DiffusionDB repository](https://github.com/poloclub/diffusiondb)
 - **Distribution:** [DiffusionDB Hugging Face Dataset](https://huggingface.co/datasets/poloclub/diffusiondb)
 - **Paper:** [DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models](https://arxiv.org/abs/2210.14896)
-- **Point of Contact:** [Jay Wang](mailto:jayw@gatech.edu)
 
 ### Dataset Summary
 
@@ -102,23 +101,22 @@ The unprecedented scale and diversity of this human-actuated dataset provide exc
 
 The text in the dataset is mostly English. It also contains other languages such as Spanish, Chinese, and Russian.
 
-### Two Subsets
+### Subset
 
-DiffusionDB provides two subsets (DiffusionDB 2M and DiffusionDB Large) to support different needs.
+DiffusionDB provides two subsets (DiffusionDB 2M and DiffusionDB Large) to support different needs. The pixelated version of the data taken from the DiffusionDB 2M and has 2000 examples only.
 
 |Subset|Num of Images|Num of Unique Prompts|Size|Image Directory|Metadata Table|
 |:--|--:|--:|--:|--:|--:|
-|DiffusionDB 2M|2M|1.5M|1.6TB|`images/`|`metadata.parquet`|
-|DiffusionDB Large|14M|1.8M|6.5TB|`diffusiondb-large-part-1/` `diffusiondb-large-part-2/`|`metadata-large.parquet`|
+|DiffusionDB-pixelart|2k|~1.5k|~1.6GB|`images/`|`metadata.parquet`|
 
-##### Key Differences
+##### Key Facts
 
 1. Two subsets have a similar number of unique prompts, but DiffusionDB Large has much more images. DiffusionDB Large is a superset of DiffusionDB 2M.
-2. Images in DiffusionDB 2M are stored in `png` format; images in DiffusionDB Large use a lossless `webp` format.
+2. Images in DiffusionDB 2M are stored in `png` format.
 
 ## Dataset Structure
 
-We use a modularized file structure to distribute DiffusionDB. The 2 million images in DiffusionDB 2M are split into 2,000 folders, where each folder contains 1,000 images and a JSON file that links these 1,000 images to their prompts and hyperparameters. Similarly, the 14 million images in DiffusionDB Large are split into 14,000 folders.
+We use a modularized file structure to distribute DiffusionDB. The 2k images in DiffusionDB-pixelart are split into folders, where each folder contains 1,000 images and a JSON file that links these 1,000 images to their prompts and hyperparameters.
 
 ```bash
 # DiffusionDB 2M
@@ -137,35 +135,7 @@ We use a modularized file structure to distribute DiffusionDB. The 2 million ima
 └── metadata.parquet
 ```
 
-```bash
-# DiffusionDB Large
-./
-├── diffusiondb-large-part-1
-│   ├── part-000001
-│   │   ├── 0a8dc864-1616-4961-ac18-3fcdf76d3b08.webp
-│   │   ├── 0a25cacb-5d91-4f27-b18a-bd423762f811.webp
-│   │   ├── 0a52d584-4211-43a0-99ef-f5640ee2fc8c.webp
-│   │   ├── [...]
-│   │   └── part-000001.json
-│   ├── part-000002
-│   ├── part-000003
-│   ├── [...]
-│   └── part-010000
-├── diffusiondb-large-part-2
-│   ├── part-010001
-│   │   ├── 0a68f671-3776-424c-91b6-c09a0dd6fc2d.webp
-│   │   ├── 0a0756e9-1249-4fe2-a21a-12c43656c7a3.webp
-│   │   ├── 0aa48f3d-f2d9-40a8-a800-c2c651ebba06.webp
-│   │   ├── [...]
-│   │   └── part-000001.json
-│   ├── part-010002
-│   ├── part-010003
-│   ├── [...]
-│   └── part-014000
-└── metadata-large.parquet
-```
-
-These sub-folders have names `part-0xxxxx`, and each image has a unique name generated by [UUID Version 4](https://en.wikipedia.org/wiki/Universally_unique_identifier). The JSON file in a sub-folder has the same name as the sub-folder. Each image is a `PNG` file (DiffusionDB 2M) or a lossless `WebP` file (DiffusionDB Large). The JSON file contains key-value pairs mapping image filenames to their prompts and hyperparameters.
+These sub-folders have names `part-0xxxxx`, and each image has a unique name generated by [UUID Version 4](https://en.wikipedia.org/wiki/Universally_unique_identifier). The JSON file in a sub-folder has the same name as the sub-folder. Each image is a `PNG` file (DiffusionDB-pixelart). The JSON file contains key-value pairs mapping image filenames to their prompts and hyperparameters.
 
 
 ### Data Instances
@@ -197,9 +167,9 @@ For example, below is the image of `f3501e05-aef7-4225-a9e9-f516527408ac.png` an
 
 ### Dataset Metadata
 
-To help you easily access prompts and other attributes of images without downloading all the Zip files, we include two metadata tables  `metadata.parquet` and `metadata-large.parquet` for DiffusionDB 2M and DiffusionDB Large, respectively.
+To help you easily access prompts and other attributes of images without downloading all the Zip files, we include a metadata table `metadata.parquet` for DiffusionDB-pixelart.
 
-The shape of `metadata.parquet` is (2000000, 13) and the shape of `metatable-large.parquet` is (14000000, 13). Two tables share the same schema, and each row represents an image. We store these tables in the Parquet format because Parquet is column-based: you can efficiently query individual columns (e.g., prompts) without reading the entire table.
+The shape of `metadata.parquet` is (2000000, 13). Two tables share the same schema, and each row represents an image. We store these tables in the Parquet format because Parquet is column-based: you can efficiently query individual columns (e.g., prompts) without reading the entire table.
 
 Below are three random rows from `metadata.parquet`.
 
@@ -211,7 +181,7 @@ Below are three random rows from `metadata.parquet`.
 
 #### Metadata Schema
 
-`metadata.parquet` and `metatable-large.parquet` share the same schema.
+`metadata.parquet` schema:
 
 |Column|Type|Description|
 |:---|:---|:---|
@@ -236,11 +206,11 @@ Below are three random rows from `metadata.parquet`.
 
 ### Data Splits
 
-For DiffusionDB 2M, we split 2 million images into 2,000 folders where each folder contains 1,000 images and a JSON file. For DiffusionDB Large, we split 14 million images into 14,000 folders where each folder contains 1,000 images and a JSON file.
+For DiffusionDB-pixelart, we split 2k images into folders where each folder contains 1,000 images and a JSON file.
 
 ### Loading Data Subsets
 
-DiffusionDB is large (1.6TB or 6.5 TB)! However, with our modularized file structure, you can easily load a desirable number of images and their prompts and hyperparameters. In the [`example-loading.ipynb`](https://github.com/poloclub/diffusiondb/blob/main/notebooks/example-loading.ipynb) notebook, we demonstrate three methods to load a subset of DiffusionDB. Below is a short summary.
+DiffusionDB is large! However, with our modularized file structure, you can easily load a desirable number of images and their prompts and hyperparameters. In the [`example-loading.ipynb`](https://github.com/poloclub/diffusiondb/blob/main/notebooks/example-loading.ipynb) notebook, we demonstrate three methods to load a subset of DiffusionDB. Below is a short summary.
 
 #### Method 1: Using Hugging Face Datasets Loader
 
@@ -251,7 +221,7 @@ import numpy as np
 from datasets import load_dataset
 
 # Load the dataset with the `large_random_1k` subset
-dataset = load_dataset('poloclub/diffusiondb', 'large_random_1k')
+dataset = load_dataset('jainr3/diffusiondb-pixelart', 'large_random_1k')
 ```
 
 #### Method 2. Use the PoloClub Downloader
@@ -402,4 +372,4 @@ The Python code in this repository is available under the [MIT License](https://
 
 ### Contributions
 
-If you have any questions, feel free to [open an issue](https://github.com/poloclub/diffusiondb/issues/new) or contact [Jay Wang](https://zijie.wang).
+If you have any questions, feel free to [open an issue](https://github.com/poloclub/diffusiondb/issues/new) or contact the original author [Jay Wang](https://zijie.wang).