Datasets:

AbstractPhil
/

mega-liminal

license: other
license_name: web-collected-images
pretty_name: Mega Liminal
task_categories:
  - text-to-image
  - image-to-text
language:
  - en
tags:
  - liminal
  - liminal-spaces
  - captioned
  - lora-training
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path:
          - data/**/*.jpg
          - data/**/*.jpeg
          - data/**/*.png
          - data/**/*.webp
          - data/metadata.jsonl

Mega Liminal

1,873 captioned images of liminal spaces: empty malls, fog-bound roads, parking garages, suburbs at night, vacant theatres and hallways, sorted into 10 classes and curated for training image models. The Mega Liminal LoRA was trained on it.

Class	Files
mega liminal	870
landscape	766
suburban	111
simulacrum	36
vanishing point	21
empty mall	20
hand sourced	18
parking garage	13
cityscape	10
movie theatre	8
total	1,873 files, 1,060 unique images

The classes overlap. The mega liminal class gathers most of the landscape images and some suburban ones, so 813 files are byte-identical copies filed under a second class. Each copy has its own caption. The also_in column names the other copies; keep one row per sha256 for a deduplicated set.

The captioner labeled 1,680 images as photographs, 122 as 3D renders, 69 as digital paintings and 2 as drawings. 55 images carry a visible watermark (watermark column).

Layout

data/<class>/<image>            the image (jpg, jpeg, png or webp)
data/<class>/<image stem>.txt   its caption
data/metadata.jsonl             one row per image: file_name, text, class, width, height, medium, watermark, sha256, also_in
omitted_manifest.csv            the 202 source images removed during curation, with the reason

Loading

With the datasets library:

from datasets import load_dataset
ds = load_dataset("AbstractPhil/mega-liminal", split="train")

For trainers that read image and .txt pairs (kohya, ai-toolkit, diffusion-pipe, OneTrainer), download the files and point the trainer at mega-liminal/data/<class>:

hf download AbstractPhil/mega-liminal --repo-type dataset --local-dir mega-liminal

Captions

Every caption has the form liminal, <class>, <description>. Qwen3.5-9B wrote the descriptions, one structured pass per image: two to four sentences covering the kind of image, the place and its layout, the camera position, the architecture and materials, and the lighting and colors. The prompt ruled out mood words and sentences about what is absent, and a cleanup pass removed any that slipped through. Some captions mention visible text or a watermark.

Curation

202 of the 2,075 source images were removed:

Reason	Images
under 512x512 pixels (262,144)	63
video-game screenshots (Garry's Mod and similar engines)	74
low-resolution 3D render	33
cartoon or toy render	17
low-poly or untextured 3D	10
pixel art	3
framed collage	2

Every removal except the pixel-count rule was checked by eye. Clean, high-resolution renders were kept.

Licensing

The images were gathered from public liminal-space collections on the web. Their rights stay with their original creators, and some carry the creator's watermark. The dataset is shared for research and non-commercial use. To have an image removed, open a discussion on this repo.

The captions were written by Qwen3.5-9B for this dataset.