--- license: cc-by-nc-sa-4.0 task_categories: - image-to-text language: - en pretty_name: FFHQ, recaptioned size_categories: - 10K Describe this picture in one or two sentences: who is in it (apparent age, expression, hair, and any eyewear, headwear or > jewelry), each visible garment with its color and any print or text on it, the background and the light. Answer with the visible > description ONLY. Do not describe print or text if none is present. Asked for "any eyewear, headwear or jewelry" and "any print or text", the model also names what is absent ("no eyewear, headwear, or jewelry", "with no visible print or text"): 68 and 36 of the first 100 captions. Three reworded asks on the same faces did not remove it (the best still wrote one in 55 of 100, with longer captions and fewer accessories named), so `caption` drops those statements with `absences.drop` from qwen-symbolic-thinking 0.1.52 and `caption_raw` keeps the original: 58,032 captions changed, 1,178 still name an absent item, most of them exceptions that name what is there ("no jewelry beyond the earrings"), kept on purpose. Other negatives stay ("no visible smile"). Median length 65 words; 290 captions reached the 160-token cap (`truncated`). **Tags.** `animetimm/eva_giant_patch14_560.dbv4-full`, every tag with its score, category and published precision; `tags_kept` applies a leave-out list (`short hair`, `see-through`, `see-through sleeves`, `see-through shirt`, `see-through clothes`, `hair bun`, `single hair bun`, `double bun`, `parted lips`, `lips`, `realistic`, `photorealistic`, `asian`, `old`, `cosplay`) and a precision floor of 0.4. The tags are a drawing vocabulary read from photographs: `1girl` / `1boy` count people, and some tags fire on ordinary photos: `cosplay` on everyday photos (19 of the first 100 faces) and `old` on young adults (10 of 100), so both are left out of `tags_kept` and stay in `tags`. **Lighting.** [CinemaCLIP](https://huggingface.co/OZU-Technology/CinemaCLIP)'s released encoder (its label schema 1.0.0) reads five lighting heads; `shot_light` takes, in order: silhouette; the key light's side (from the left, from the right, from above, backlit; left and right are the picture's; front and ambient name none); rim light (edge light, unless backlit); low key (high contrast, mean luminance under 0.35) or high key (low contrast, over 0.65). The head's `under` label names nothing: of the four faces among the first 100 read as lit from below, the three checked by eye on a sheet were frontal flash shots (the `raw/` lighting run still carries "from below" from the first rule). By eye on that sheet, high key (6) and low key (1) were right, and from above, from the left and from the right were right or generic (daylight from above). Counts over every face: from above 15,436, from the right 3,341, high key 2,381, rim light 1,353, low key 1,101, from the left 1,033, backlit 63; none 47,303. The reader names the right of the picture about three times as often as the left (3,341 against 1,033); the faces checked by eye were lit from the side it named, but the imbalance itself is unexplained and may be the reader's own. FFHQ holds people of every age, children and babies included; the captions give the apparent age as seen. Code: [github AbstractEyes/qwen-symbolic-thinking](https://github.com/AbstractEyes/qwen-symbolic-thinking) (`captions.run`, `lighting.heads_run` v0.1.49; `absences.drop` v0.1.52, the light rule v0.1.50; `notebooks/ffhq_captions.ipynb`). The faces were read from [gaunernst/ffhq-1024-wds](https://huggingface.co/datasets/gaunernst/ffhq-1024-wds), FFHQ's `images1024x1024` transcoded losslessly to WebP under the original numbers. ## Uses Portrait captioning and caption evaluation, tag prediction against a booru vocabulary, lighting-aware captioning, and conditioning research for image models. Not for the development or improvement of facial recognition technologies (FFHQ's terms), and nothing here names or identifies anyone. ## License CC BY-NC-SA 4.0, as FFHQ itself: non-commercial use, credit by citing the paper below, changes indicated, and works built on this shared under the same license. FFHQ's images are not here; each keeps its own Flickr license (Creative Commons BY 2.0, BY-NC 2.0, Public Domain Mark 1.0, CC0 1.0 or U.S. Government Works), which FFHQ's metadata names per image. Made with [Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) (Apache-2.0), [animetimm/eva_giant_patch14_560.dbv4-full](https://huggingface.co/animetimm/eva_giant_patch14_560.dbv4-full) (GPL-3.0) and [CinemaCLIP](https://huggingface.co/OZU-Technology/CinemaCLIP) (OpenRAIL-M). ## Privacy FFHQ lets photo owners leave it (the `no_cv` tag on Flickr, a license change, removal, or a request to NVIDIA), and NVIDIA updates the set. The rows here are keyed by image number so that a face removed from FFHQ can be removed here too: open a discussion on this repository with the image number. ## Citation ```bibtex @article{karras2018stylegan, title = {A Style-Based Generator Architecture for Generative Adversarial Networks}, author = {Karras, Tero and Laine, Samuli and Aila, Timo}, journal = {arXiv preprint arXiv:1812.04948}, year = {2018} } ```