Update README.md

README.md

@@ -87,7 +87,7 @@ task_ids:
 
 ### Dataset Summary
 
-This is a subset of the DiffusionDB dataset which has been turned into pixel-style art.
+**This is a subset of the DiffusionDB 2M dataset which has been turned into pixel-style art.**
 
 DiffusionDB is the first large-scale text-to-image prompt dataset. It contains **14 million** images generated by Stable Diffusion using prompts and hyperparameters specified by real users.
 
@@ -103,23 +103,20 @@ The text in the dataset is mostly English. It also contains other languages such
 
 ### Subset
 
-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.
+DiffusionDB provides two subsets (DiffusionDB 2M and DiffusionDB Large) to support different needs. The pixelated version of the data was taken from the DiffusionDB 2M and has 2000 examples only.
 
 |Subset|Num of Images|Num of Unique Prompts|Size|Image Directory|Metadata Table|
 |:--|--:|--:|--:|--:|--:|
 |DiffusionDB-pixelart|2k|~1.5k|~1.6GB|`images/`|`metadata.parquet`|
 
-##### 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-pixelart are stored in `png` format.
 
 ## Dataset Structure
 
 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
+# DiffusionDB 2k
 ./
 ├── images
 │   ├── part-000001
@@ -224,72 +221,6 @@ from datasets import load_dataset
 dataset = load_dataset('jainr3/diffusiondb-pixelart', 'large_random_1k')
 ```
 
-#### Method 2. Use the PoloClub Downloader
-
-This repo includes a Python downloader [`download.py`](https://github.com/poloclub/diffusiondb/blob/main/scripts/download.py) that allows you to download and load DiffusionDB. You can use it from your command line. Below is an example of loading a subset of DiffusionDB.
-
-##### Usage/Examples
-
-The script is run using command-line arguments as follows:
-
-- `-i` `--index` - File to download or lower bound of a range of files if `-r` is also set.
-- `-r` `--range` - Upper bound of range of files to download if `-i` is set.
-- `-o` `--output` - Name of custom output directory. Defaults to the current directory if not set.
-- `-z` `--unzip` - Unzip the file/files after downloading
-- `-l` `--large` - Download from Diffusion DB Large. Defaults to Diffusion DB 2M.
-
-###### Downloading a single file
-
-The specific file to download is supplied as the number at the end of the file on HuggingFace. The script will automatically pad the number out and generate the URL.
-
-```bash
-python download.py -i 23
-```
-
-###### Downloading a range of files
-
-The upper and lower bounds of the set of files to download are set by the `-i` and `-r` flags respectively.
-
-```bash
-python download.py -i 1 -r 2000
-```
-
-Note that this range will download the entire dataset. The script will ask you to confirm that you have 1.7Tb free at the download destination.
-
-###### Downloading to a specific directory
-
-The script will default to the location of the dataset's `part` .zip files at `images/`. If you wish to move the download location, you should move these files as well or use a symbolic link.
-
-```bash
-python download.py -i 1 -r 2000 -o /home/$USER/datahoarding/etc
-```
-
-Again, the script will automatically add the `/` between the directory and the file when it downloads.
-
-###### Setting the files to unzip once they've been downloaded
-
-The script is set to unzip the files _after_ all files have downloaded as both can be lengthy processes in certain circumstances.
-
-```bash
-python download.py -i 1 -r 2000 -z
-```
-
-#### Method 3. Use `metadata.parquet` (Text Only)
-
-If your task does not require images, then you can easily access all 2 million prompts and hyperparameters in the `metadata.parquet` table.
-
-```python
-from urllib.request import urlretrieve
-import pandas as pd
-
-# Download the parquet table
-table_url = f'https://huggingface.co/datasets/poloclub/diffusiondb/resolve/main/metadata.parquet'
-urlretrieve(table_url, 'metadata.parquet')
-
-# Read the table using Pandas
-metadata_df = pd.read_parquet('metadata.parquet')
-```
-
 ## Dataset Creation
 
 ### Curation Rationale