glennwuwu/tiktok-techjam-2026-openfake-train
TikTok TechJam 2026 — OpenFake train slice Filtered, re-encoded train split used by Seer. This is not a copy of ComplexDataLab/OpenFake (3.44 TB). It is the on-disk mixture source openfake/train. Do not use core/test or reddit/test from this repo — those holdouts stay out of training. Contents 439,523 JPEGs, ~102 GB. Images were streamed from OpenFake core/train, filtered to 30 generators + two real pools, resized to max_side=1536, JPEG quality 95. split… See the full description on the dataset page: https://huggingface.co/datasets/glennwuwu/tiktok-techjam-2026-openfake-train.
TikTok TechJam 2026 — OpenFake train slice
Filtered, re-encoded train split used by Seer. This is not a copy of `ComplexDataLab/OpenFake` (3.44 TB). It is the on-disk mixture source openfake/train.
Do not use core/test or reddit/test from this repo — those holdouts stay out of training.
Contents
439,523 JPEGs, ~102 GB. Images were streamed from OpenFake core/train, filtered to 30 generators + two real pools, resized to max_side=1536, JPEG quality 95.
Generator caps came from rank.json (recall of an earlier Seer checkpoint) with tiers 0.70=25000 0.95=15000 0.98=10000 and --cap-model pexels=80000 laion=50000. Some models have fewer images than their cap (upstream pool exhausted). _meta/counts.json and _meta/plan.json are the exact plan and realized counts.
Load
from huggingface_hub import snapshot_download
root = snapshot_download(
"glennwuwu/tiktok-techjam-2026-openfake-train",
repo_type="dataset",
)
# then point Seer at:
# fake_dirs: [f"{root}/fake"]
# real_dirs: [f"{root}/real"]Licence / attribution
Redistribution of a subset of OpenFake (cc-by-sa-4.0). Proprietary-generator subsets are non-commercial. Cite ComplexDataLab/OpenFake if you use this slice.