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label
class label 200
classes | latent
sequencelengths 4
4
|
|---|---|
0001.Black_footed_Albatross
| [[[1.1962890625,0.93359375,1.154296875,0.2027587890625,0.54052734375,1.8212890625,0.8408203125,1.317(...TRUNCATED) |
0001.Black_footed_Albatross
| [[[-0.363525390625,0.537109375,0.380615234375,0.88525390625,1.640625,1.6533203125,0.6513671875,-0.18(...TRUNCATED) |
0001.Black_footed_Albatross
| [[[0.86083984375,1.0771484375,2.123046875,0.97900390625,1.4287109375,1.1171875,1.4697265625,1.176757(...TRUNCATED) |
0001.Black_footed_Albatross
| [[[0.65087890625,0.16357421875,-0.81396484375,-0.488037109375,-0.47607421875,0.333740234375,0.152465(...TRUNCATED) |
0001.Black_footed_Albatross
| [[[1.3134765625,1.046875,0.85791015625,0.12255859375,1.1240234375,0.73681640625,1.0869140625,1.66308(...TRUNCATED) |
0001.Black_footed_Albatross
| [[[0.953125,1.3173828125,1.3564453125,1.1982421875,1.19140625,1.0830078125,0.734375,0.89794921875,0.(...TRUNCATED) |
0001.Black_footed_Albatross
| [[[1.2880859375,1.1484375,1.173828125,1.201171875,1.19140625,1.18359375,1.185546875,1.177734375,1.18(...TRUNCATED) |
0001.Black_footed_Albatross
| [[[0.10565185546875,1.6103515625,2.603515625,1.5380859375,2.310546875,2.1171875,1.693359375,2.457031(...TRUNCATED) |
0001.Black_footed_Albatross
| [[[0.136474609375,1.1015625,1.3818359375,0.88623046875,1.009765625,0.6923828125,0.005313873291015625(...TRUNCATED) |
0001.Black_footed_Albatross
| [[[2.1953125,1.0712890625,2.21484375,1.275390625,1.771484375,1.322265625,1.08984375,1.751953125,0.84(...TRUNCATED) |
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Dataset Card for cub2011-latent-64
This dataset includes the latent vectors calculated by:
- CUB2011 images
- Resample 512 x 512
- Encode with vae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae", torch_dtype=torch.float16)
The output are 64x64 images with 4 channels.
Dataset Details
When using it should be loaded as follows:
from diffusers import AutoencoderKL
import torch
vae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae", torch_dtype=torch.float16)
dataset.set_format('torch', columns=['latent'], output_all_columns=True)
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