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RS-Dataset-Latents

Latent representations of remote sensing images encoded using pre-trained VAEs. Part of the VAEs4RS project.

Structure

RS-Dataset-Latents/
├── FLUX1-VAE/UCMerced.zip
├── FLUX2-VAE/UCMerced.zip
├── Qwen-VAE/UCMerced.zip
├── SANA-VAE/UCMerced.zip
├── SD21-VAE/UCMerced.zip
├── SD35-VAE/UCMerced.zip
└── SDXL-VAE/UCMerced.zip

Each zip file contains .npz files with latent representations. Each .npz file contains:

latent: float16 array with shape (channels, height, width)

Usage

import zipfile
import numpy as np

# Load from zip file
with zipfile.ZipFile("FLUX1-VAE/UCMerced.zip", "r") as z:
    # List files
    files = z.namelist()
    
    # Load a specific file
    with z.open("UCMerced/agricultural00.npz") as f:
        data = np.load(f)
        latent = data['latent']  # shape: (16, 32, 32), dtype: float16

VAE Models

Model	Channels	Compression	Scaling Factor
SD21-VAE	4	8×	0.18215
SDXL-VAE	4	8×	0.13025
SD35-VAE	16	8×	1.5305
FLUX1-VAE	16	8×	0.3611
FLUX2-VAE	32	8×	0.3611
SANA-VAE	32	32×	0.41407
Qwen-VAE	16	8×	0.41407

Citation

@article{chen2026robustness,
  author = {Zhenyuan Chen and Feng Zhang},
  title = {The Robustness of Natural English Priors in Remote Sensing: A Zero-Shot VAE Study},
  year = {2026}
}

RS-Dataset-Latents

Structure

Usage

VAE Models

Citation

Links