Datasets:

nateraw
/

midjourney-texttoimage-new

License:

Dataset card Files Files and versions Community

Dataset Viewer

Full Screen Viewer

Full Screen

Split (1)

train

The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ValueError
Message:      Not able to read records in the JSON file at hf://datasets/nateraw/midjourney-texttoimage-new@e0c29cfa541e8a082ce6ee1c9bec75d37333a98d/general-01_2022_06_20.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['total_results', 'messages', 'analytics_id', 'threads', 'members']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. 
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 240, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2216, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1239, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__
                  yield from islice(self.ex_iterable, self.n)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__
                  for key, pa_table in self.generate_tables_fn(**self.kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 170, in _generate_tables
                  raise ValueError(
              ValueError: Not able to read records in the JSON file at hf://datasets/nateraw/midjourney-texttoimage-new@e0c29cfa541e8a082ce6ee1c9bec75d37333a98d/general-01_2022_06_20.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['total_results', 'messages', 'analytics_id', 'threads', 'members']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Dataset Card for Midjourney User Prompts & Generated Images (250k)

Dataset Summary

General Context

Midjourney is an independent research lab whose broad mission is to "explore new mediums of thought". In 2022, they launched a text-to-image service that, given a natural language prompt, produces visual depictions that are faithful to the description. Their service is accessible via a public Discord server, where users interact with a Midjourney bot. When issued a query in natural language, the bot returns four low-resolution images and offers further options like upscaling or re-generating a variation of the original images.

This dataset was obtained by scraping messages from the public Discord server over a period of four weeks (June 20, 2002 - July 17, 2022). The authors have no affiliation with Midjourney and are releasing this data with the sole purpose of enabling research on text-to-image model prompting (see the Sample Use Case section below).

Midjourney's Discord Server

Here is what the interaction with the Midjourney bot looks like on Discord:

Issuing an initial prompt:
Upscaling the bottom-left image:
Requesting variations of the bottom-left image:

Dataset Format

The dataset was produced by scraping ten public Discord channels in the "general" category (i.e., with no dedicated topic) over four weeks. Filenames follow the pattern channel-name_yyyy_mm_dd.json. The "messages" field in each JSON file contains a list of Message objects, one per user query. A message includes information such as the user-issued prompt, a link to the generated image, and other metadata. See the companion notebook with utilities for extracting such information.

User Prompt	Generated Image URL
anatomical heart fill with deers, neon, pastel, artstation	https://cdn.discordapp.com/attachments/985204969722486814/989673529102463016/f14d5cb4-aa4d-4060-b017-5ee6c1db42d6_Ko_anatomical_heart_fill_with_deers_neon_pastel_artstation.png
anatomical heart fill with jumping running deers, neon, pastel, artstation	https://cdn.discordapp.com/attachments/985204969722486814/989675045439815721/1d7541f2-b659-4a74-86a3-ae211918723c_Ko_anatomical_heart_fill_with_jumping_running_deers_neon_pastel_artstation.png
https://s.mj.run/UlkFmVAKfaE cat with many eyes floating in colorful glowing swirling whisps, occult inspired, emerging from the void, shallow depth of field	https://cdn.discordapp.com/attachments/982990243621908480/988957623229501470/6116dc5f-64bb-4afb-ba5f-95128645c247_MissTwistedRose_cat_with_many_eyes_floating_in_colorful_glowing_swirling_whisps_occult_inspired_emerging_from_the_vo.png

Dataset Stats

The dataset contains: - 268k messages from 10 public Discord channel collected over 28 days. - 248k user-generated prompts and their associated generated images, out of which: + 60% are requests for new images (initial or variation requests for a previously-generated image), and + 40% are requests for upscaling previously-generated images.
Prompt Analysis

Here are the most prominent phrases among the user-generated text prompts:

Prompt lengths span from 1 to 60 whitespace-separated tokens, with the mode around 15 tokens:

See the the companion notebook for an in-depth analysis of how users control various aspects of the generated images (lighting, resolution, photographic elements, artistic style, etc.).

Sample Use Case

One way of leveraging this dataset is to help address the prompt engineering problem: artists that use text-to-image models in their work spend a significant amount of time carefully crafting their text prompts. We built an additional model for prompt autocompletion by learning from the queries issued by Midjourney users. This notebook shows how to extract the natural language prompts from the Discord messages and create a HuggingFace dataset to be used for training. The processed dataset can be found at succinctly/midjourney-prompts, and the prompt generator (a GPT-2 model fine-tuned on prompts) is located at succinctly/text2image-prompt-generator.

Here is how our model can help brainstorm creative prompts and speed up prompt engineering:

Authors

This project was a collaboration between Iulia Turc and Gaurav Nemade. We recently left Google Research to work on something new. Feel free to Tweet at us, or follow our journey at succinctly.ai.

Interesting Finds

Here are some of the generated images that drew our attention:

User Prompt	Generated Image
https://s.mj.run/JlwNbH Historic Ensemble of the Potala Palace Lhasa, japanese style painting,trending on artstation, temple, architecture, fiction, sci-fi, underwater city, Atlantis , cyberpunk style, 8k revolution, Aokigahara fall background , dramatic lighting, epic, photorealistic, in his lowest existential moment with high detail, trending on artstation,cinematic light, volumetric shading ,high radiosity , high quality, form shadow, rim lights , concept art of architecture, 3D,hyper deatiled,very high quality,8k,Maxon cinema,visionary,imaginary,realistic,as trending on the imagination of Gustave Doré idea,perspective view,ornate light --w 1920 --h 1024
a dark night with fog in a metropolis of tomorrow by hugh ferriss:, epic composition, maximum detail, Westworld, Elysium space station, space craft shuttle, star trek enterprise interior, moody, peaceful, hyper detailed, neon lighting, populated, minimalist design, monochromatic, rule of thirds, photorealistic, alien world, concept art, sci-fi, artstation, photorealistic, arch viz , volumetric light moody cinematic epic, 3d render, octane render, trending on artstation, in the style of dylan cole + syd mead + by zaha hadid, zaha hadid architecture + reaction-diffusion + poly-symmetric + parametric modelling, open plan, minimalist design 4k --ar 3:1
https://s.mj.run/qKj8n0 fantasy art, hyperdetailed, panoramic view, foreground is a crowd of ancient Aztec robots are doing street dance battle , main part is middleground is majestic elegant Gundam mecha robot design with black power armor and unsettling ancient Aztec plumes and decorations scary looking with two magical neon swords combat fighting::2 , background is at night with nebula eruption, Rembrandt lighting, global illumination, high details, hyper quality, unreal negine, octane render, arnold render, vray render, photorealistic, 8k --ar 3:1 --no dof,blur,bokeh
https://s.mj.run/zMIhrKBDBww in side a Amethyst geode cave, 8K symmetrical portrait, trending in artstation, epic, fantasy, Klimt, Monet, clean brush stroke, realistic highly detailed, wide angle view, 8k post-processing highly detailed, moody lighting rendered by octane engine, artstation,cinematic lighting, intricate details, 8k detail post processing, --no face --w 512 --h 256
https://s.mj.run/GTuMoq whimsically designed gothic, interior of a baroque cathedral in fire with moths and birds flying, rain inside, with angels, beautiful woman dressed with lace victorian and plague mask, moody light, 8K photgraphy trending on shotdeck, cinema lighting, simon stålenhag, hyper realistic octane render, octane render, 4k post processing is very detailed, moody lighting, Maya+V-Ray +metal art+ extremely detailed, beautiful, unreal engine, lovecraft, Big Bang cosmology in LSD+IPAK,4K, beatiful art by Lêon François Comerre, ashley wood, craig mullins, ,outer space view, William-Adolphe Bouguereau, Rosetti --w 1040 --h 2080