Update reademe

README.md

@@ -33,7 +33,7 @@ task_ids:
 
 # DiffusionDB
 
-<img width="100%" src="https://user-images.githubusercontent.com/15007159/198505835-bcc3a34f-a782-4064-989b-135e32b577a7.gif">
+<img width="100%" src="https://user-images.githubusercontent.com/15007159/201762588-f24db2b8-dbb2-4a94-947b-7de393fc3d33.gif">
 
 ## Table of Contents
 
@@ -43,9 +43,13 @@ task_ids:
     - [Dataset Summary](#dataset-summary)
     - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
     - [Languages](#languages)
+    - [Two Subsets](#two-subsets)
+        - [Key Differences](#key-differences)
   - [Dataset Structure](#dataset-structure)
     - [Data Instances](#data-instances)
     - [Data Fields](#data-fields)
+    - [Dataset Metadata](#dataset-metadata)
+      - [Metadata Schema](#metadata-schema)
     - [Data Splits](#data-splits)
     - [Loading Data Subsets](#loading-data-subsets)
       - [Method 1: Using Hugging Face Datasets Loader](#method-1-using-hugging-face-datasets-loader)
@@ -85,7 +89,7 @@ task_ids:
 
 ### Dataset Summary
 
-DiffusionDB is the first large-scale text-to-image prompt dataset. It contains 2 million images generated by Stable Diffusion using prompts and hyperparameters specified by real users.
+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.
 
 DiffusionDB is publicly available at [🤗 Hugging Face Dataset](https://huggingface.co/datasets/poloclub/diffusiondb).
 
@@ -97,30 +101,71 @@ 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
+
+DiffusionDB provides two subsets (DiffusionDB 2M and DiffusionDB Large) to support different needs.
+
+|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`|
+
+##### Key Differences
+
+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.
+
 ## Dataset Structure
 
-We use a modularized file structure to distribute DiffusionDB. The 2 million images in DiffusionDB 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.
+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.
 
 ```bash
+# DiffusionDB 2M
 ./
 ├── images
 │   ├── part-000001
 │   │   ├── 3bfcd9cf-26ea-4303-bbe1-b095853f5360.png
 │   │   ├── 5f47c66c-51d4-4f2c-a872-a68518f44adb.png
 │   │   ├── 66b428b9-55dc-4907-b116-55aaa887de30.png
-│   │   ├── 99c36256-2c20-40ac-8e83-8369e9a28f32.png
-│   │   ├── f3501e05-aef7-4225-a9e9-f516527408ac.png
 │   │   ├── [...]
 │   │   └── part-000001.json
 │   ├── part-000002
 │   ├── part-000003
-│   ├── part-000004
 │   ├── [...]
 │   └── part-002000
 └── metadata.parquet
 ```
 
-These sub-folders have names `part-00xxxx`, 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. The JSON file contains key-value pairs mapping image filenames to their prompts and hyperparameters.
+```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.
+
 
 ### Data Instances
 
@@ -149,38 +194,50 @@ For example, below is the image of `f3501e05-aef7-4225-a9e9-f516527408ac.png` an
 - `st`: Steps
 - `sa`: Sampler
 
-At the top level folder of DiffusionDB, we include a metadata table in Parquet format `metadata.parquet`.
-This table has seven columns: `image_name`, `prompt`, `part_id`, `seed`, `step`, `cfg`, and `sampler`, and it has 2 million rows where each row represents an image. `seed`, `step`, and `cfg` are We choose Parquet because it is column-based: researchers can efficiently query individual columns (e.g., prompts) without reading the entire table. Below are the five random rows from the table.
-
-| image_name                               | prompt                                                                                                                                                                                                  | part_id | seed       | step | cfg | sampler |
-|------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------|------------|------|-----|---------|
-| 49f1e478-ade6-49a8-a672-6e06c78d45fc.png | ryan gosling in fallout 4 kneels near a nuclear bomb                                                                                                                                                    | 1643    | 2220670173 | 50   | 7.0 | 8       |
-| b7d928b6-d065-4e81-bc0c-9d244fd65d0b.png | A beautiful robotic woman dreaming, cinematic lighting, soft bokeh, sci-fi, modern, colourful, highly detailed, digital painting, artstation, concept art, sharp focus, illustration, by greg rutkowski | 87      | 51324658   | 130  | 6.0 | 8       |
-| 19b1b2f1-440e-4588-ba96-1ac19888c4ba.png | bestiary of creatures from the depths of the unconscious psyche, in the style of a macro photograph with shallow dof                                                                                    | 754     | 3953796708 | 50   | 7.0 | 8       |
-| d34afa9d-cf06-470f-9fce-2efa0e564a13.png | close up portrait of one calico cat by vermeer. black background, three - point lighting, enchanting, realistic features, realistic proportions.                                                        | 1685    | 2007372353 | 50   | 7.0 | 8       |
-| c3a21f1f-8651-4a58-a4d4-7500d97651dc.png | a bottle of jack daniels with the word medicare replacing the word jack daniels                                                                                                                         | 243     | 1617291079 | 50   | 7.0 | 8       |
-
-To save space, we use an integer to encode the `sampler` in the table above.
-
-|Sampler|Integer Value|
-|:--|--:|
-|ddim|1|
-|plms|2|
-|k_euler|3|
-|k_euler_ancestral|4|
-|ddik_heunm|5|
-|k_dpm_2|6|
-|k_dpm_2_ancestral|7|
-|k_lms|8|
-|others|9|
+### 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.
+
+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.
+
+Below are three random rows from `metadata.parquet`.
+
+| image_name                               | prompt                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |   part_id |       seed |   step |   cfg |   sampler |   width |   height | user_name                                                        | timestamp                 |   image_nsfw |   prompt_nsfw |
+|:-----------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------:|-----------:|-------:|------:|----------:|--------:|---------:|:-----------------------------------------------------------------|:--------------------------|-------------:|--------------:|
+| 0c46f719-1679-4c64-9ba9-f181e0eae811.png | a small liquid sculpture, corvette, viscous, reflective, digital art                                                                                                                                                                                                                                                                                                                                                                                                           |      1050 | 2026845913 |     50 |   7   |         8 |     512 |      512 | c2f288a2ba9df65c38386ffaaf7749106fed29311835b63d578405db9dbcafdb | 2022-08-11 09:05:00+00:00 |    0.0845108 |   0.00383462  |
+| a00bdeaa-14eb-4f6c-a303-97732177eae9.png | human sculpture of lanky tall alien on a romantic date at italian restaurant with smiling woman, nice restaurant, photography, bokeh                                                                                                                                                                                                                                                                                                                                           |       905 | 1183522603 |     50 |  10   |         8 |     512 |      768 | df778e253e6d32168eb22279a9776b3cde107cc82da05517dd6d114724918651 | 2022-08-19 17:55:00+00:00 |    0.692934  |   0.109437    |
+| 6e5024ce-65ed-47f3-b296-edb2813e3c5b.png | portrait of barbaric spanish conquistador, symmetrical, by yoichi hatakenaka, studio ghibli and dan mumford                                                                                                                                                                                                                                                                                                                                                                    |       286 | 1713292358 |     50 |   7   |         8 |     512 |      640 | 1c2e93cfb1430adbd956be9c690705fe295cbee7d9ac12de1953ce5e76d89906 | 2022-08-12 03:26:00+00:00 |    0.0773138 |   0.0249675   |
+
+#### Metadata Schema
+
+`metadata.parquet` and `metatable-large.parquet` share the same schema.
+
+|Column|Type|Description|
+|:---|:---|:---|
+|`image_name`|`string`|Image UUID filename.|
+|`prompt`|`string`|The text prompt used to generate this image.|
+|`part_id`|`uint16`|Folder ID of this image.|
+|`seed`|`uint32`| Random seed used to generate this image.|
+|`step`|`uint16`| Step count (hyperparameter).|
+|`cfg`|`float32`| Guidance scale (hyperparameter).|
+|`sampler`|`uint8`| Sampler method (hyperparameter). Mapping: {1: "ddim", 2: "plms", 3: "k_euler", 4: "k_euler_ancestral", 5: "k_heun", 6: "k_dpm_2", 7: "k_dpm_2_ancestral", 8: "k_lms", 9: "others"}'.
+|`width`|`uint16`|Image width.|
+|`height`|`uint16`|Image height.|
+|`user_name`|`string`|The unique discord ID's SHA256 hash of the user who generated this image. For example, the hash for `xiaohk#3146` is `e285b7ef63be99e9107cecd79b280bde602f17e0ca8363cb7a0889b67f0b5ed0`. "deleted_account" refer to users who have deleted their accounts. None means the image has been deleted before we scrape it for the second time.|
+|`timestamp`|`timestamp`|UTC Timestamp when this image was generated. None means the image has been deleted before we scrape it for the second time. Note that timestamp is not accurate for duplicate images that have the same prompt, hypareparameters, width, height.|
+|`image_nsfw`|`float32`|Likelihood of an image being NSFW. Scores are predicted by [LAION's state-of-art NSFW detector](https://github.com/LAION-AI/LAION-SAFETY) (range from 0 to 1). A score of 2.0 means the image has already been flagged as NSFW and blurred by Stable Diffusion.|
+|`prompt_nsfw`|`float32`|Likelihood of a prompt being NSFW. Scores are predicted by the library [Detoxicy](https://github.com/unitaryai/detoxify). Each score represents the maximum of `toxicity` and `sexual_explicit` (range from 0 to 1).|
+
+> **Warning**
+> Although the Stable Diffusion model has an NSFW filter that automatically blurs user-generated NSFW images, this NSFW filter is not perfect—DiffusionDB still contains some NSFW images. Therefore, we compute and provide the NSFW scores for images and prompts using the state-of-the-art models. The distribution of these scores can be found in our [research paper](https://arxiv.org/abs/2210.14896). Please decide an appropriate NSFW score threshold to filter out NSFW images before using DiffusionDB in your projects.
 
 ### Data Splits
 
-We split 2 million images into 2,000 folders where each folder contains 1,000 images and a JSON file.
+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.
 
 ### Loading Data Subsets
 
-DiffusionDB is large (1.6TB)! 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 (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.
 
 #### Method 1: Using Hugging Face Datasets Loader
 
@@ -198,7 +255,7 @@ dataset = load_dataset('poloclub/diffusiondb', 'random_1k')
 
 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
+##### Usage/Examples
 
 The script is run using command-line arguments as follows:
 
@@ -206,6 +263,7 @@ The script is run using command-line arguments as follows:
 - `-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
 
@@ -268,7 +326,7 @@ Recent diffusion models have gained immense popularity by enabling high-quality
 However, generating images with desired details is difficult, as it requires users to write proper prompts specifying the exact expected results. Developing such prompts requires trial and error, and can often feel random and unprincipled. Simon Willison analogizes writing prompts to wizards learning “magical spells”: users do not understand why some prompts work, but they will add these prompts to their “spell book.” For example, to generate highly-detailed images, it has become a common practice to add special keywords such as “trending on artstation” and “unreal engine” in the prompt.
 
 Prompt engineering has become a field of study in the context of text-to-text generation, where researchers systematically investigate how to construct prompts to effectively solve different down-stream tasks. As large text-to-image models are relatively new, there is a pressing need to understand how these models react to prompts, how to write effective prompts, and how to design tools to help users generate images.
-To help researchers tackle these critical challenges, we create DiffusionDB, the first large-scale prompt dataset with 2 million real prompt-image pairs.
+To help researchers tackle these critical challenges, we create DiffusionDB, the first large-scale prompt dataset with 14 million real prompt-image pairs.
 
 ### Source Data
 
@@ -308,7 +366,7 @@ It should note that we collect images and their prompts from the Stable Diffusio
 
 ### Discussion of Biases
 
-The 2 million images in DiffusionDB have diverse styles and categories. However, Discord can be a biased data source. Our images come from channels where early users could use a bot to use Stable Diffusion before release. As these users had started using Stable Diffusion before the model was public, we hypothesize that they are AI art enthusiasts and are likely to have experience with other text-to-image generative models. Therefore, the prompting style in DiffusionDB might not represent novice users. Similarly, the prompts in DiffusionDB might not generalize to domains that require specific knowledge, such as medical images.
+The 14 million images in DiffusionDB have diverse styles and categories. However, Discord can be a biased data source. Our images come from channels where early users could use a bot to use Stable Diffusion before release. As these users had started using Stable Diffusion before the model was public, we hypothesize that they are AI art enthusiasts and are likely to have experience with other text-to-image generative models. Therefore, the prompting style in DiffusionDB might not represent novice users. Similarly, the prompts in DiffusionDB might not generalize to domains that require specific knowledge, such as medical images.
 
 ### Other Known Limitations
 
@@ -319,7 +377,7 @@ Therefore, different models can need users to write different prompts. For examp
 
 ### Dataset Curators
 
-DiffusionDB is created by [Jay Wang](https://zijie/wang), [Evan Montoya](https://www.linkedin.com/in/evan-montoya-b252391b4/), [David Munechika](https://www.linkedin.com/in/dmunechika/), [Alex Yang](https://alexanderyang.me), [Ben Hoover](https://www.bhoov.com), [Polo Chau](https://faculty.cc.gatech.edu/~dchau/).
+DiffusionDB is created by [Jay Wang](https://zijie.wang), [Evan Montoya](https://www.linkedin.com/in/evan-montoya-b252391b4/), [David Munechika](https://www.linkedin.com/in/dmunechika/), [Alex Yang](https://alexanderyang.me), [Ben Hoover](https://www.bhoov.com), [Polo Chau](https://faculty.cc.gatech.edu/~dchau/).
 
 
 ### Licensing Information