main-horse/in1k.int8

it's like imagenet.int8 but train+val in1k (1,331,168 samples) flux-dev vae, latent (after dequant) channels scaled to N(0,1) quantization uses int8 not uint8 (scaling factor 127/4) basic decode test: # huggingface-cli download --repo-type dataset main-horse/in1k.int8 --revision flux-1.0-dev --local-dir ./imagenet_int8 import torch from streaming import StreamingDataset import streaming.base.util as util from diffusers import AutoencoderKL from diffusers.image_processor import… See the full description on the dataset page: https://huggingface.co/datasets/main-horse/in1k.int8.