Sphere Encoder 2
Sphere Encoder 2 is a standalone autoencoder that generates images by decoding random points from a high-dimensional latent sphere. This repository hosts the pretrained checkpoints, the FDr6 reference statistics and the dataset json files used by the official code.
Models
| Model | flowers-256px | flowers-512px | imagenet-256px | imagenet-512px |
|---|---|---|---|---|
| Sphere2-B | ckpt | ckpt | ckpt | ckpt |
| Sphere2-L | ckpt | ckpt | ckpt | ckpt |
Models trained with the FD-lite loss (Sec. B.1 of the paper):
| Model | imagenet-256px | imagenet-512px |
|---|---|---|
| Sphere2-B† | ckpt | ckpt |
| Sphere2-L† | ckpt | ckpt |
Files
| Folder | Content |
|---|---|
experiments | one folder per model, with cfg.json and the checkpoint |
fdr6_stats | FDr6 reference statistics for Oxford Flowers and ImageNet, at 256px and 512px |
imagenet.json | train.json, val.json and folder_to_id_to_label.json for ImageNet |
flowers.json | train.json and val.json for Oxford Flowers |
Usage
Download a model folder into workspace/experiments of the code repository, then follow its README for sampling, evaluation and training:
hf download tomg-group-umd/sphere2 --include "experiments/sphere2-large-imagenet-512px/*" --local-dir workspace
bash scripts/sample_imagenet.sh