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---
size_categories:
- 1M<n<10M
viewer: false
license: apache-2.0
---
# Tiny Cosmos-Tokenized Imagenet
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6311151c64939fabc00c8436/2Wrz6bzvwIHVATbtYAujs.png" alt="small" width="800">
</p>
Similar fashion to [Simo's Imagenet.int8](https://github.com/cloneofsimo/imagenet.int8), here we provide [Cosmos-tokenized](https://github.com/NVIDIA/Cosmos-Tokenizer) imagenet for rapid prototyping.
Noticeably, the discrete tokenizer is able to compress entire imagenet into **shocking 2.45 GB of data!**
# How to use
This time, we dumped it all on simple pytorch safetensor format.
```python
import torch
import torch.nn as nn
from safetensors.torch import safe_open
# for continuous tokenizer
with safe_open("tokenize_dataset/imagenet_ci8x8.safetensors", framework="pt") as f:
data = f.get_tensor("latents") * 16.0 / 255.0
labels = f.get_tensor("labels")
print(data.shape) # 1281167, 16, 32, 32
print(labels.shape) # 1281167
```
To decode, you would need to install cosmos tokenizer.
```bash
git clone https://github.com/NVIDIA/Cosmos-Tokenizer.git
cd Cosmos-Tokenizer
apt-get install -y ffmpeg
pip install -e .
```
And decode using either `"Cosmos-Tokenizer-CI8x8"` or `"Cosmos-Tokenizer-DI8x8"`
**IMPORTANT**
* For continuous token, we've quantized & normalized to int8 format. Thus, you need to multiply 16.0 / 255.0
* For discrete token, saved format is int16. To use it properly just do uint16. Example below:
```python
model_name = "Cosmos-Tokenizer-CI8x8" if is_continuous else "Cosmos-Tokenizer-DI8x8"
decoder = ImageTokenizer(
checkpoint_dec=f"pretrained_ckpts/{model_name}/decoder.jit"
).to(device)
with safe_open("imagenet_ci8x8.safetensors", framework="pt") as f:
if tokenizer_type == "continuous":
data = f.get_tensor("latents").to(torch.bfloat16) * 16.0 / 255.0
else:
data = f.get_tensor("indices").to(torch.uint16)
labels = f.get_tensor("labels")
data = data[:1]
if is_continuous:
data = data.reshape(1, 16, 32, 32).to(device)
else:
# For discrete tokenizer, reshape to [1, 32, 32]
data = data.reshape(1, 32, 32).to(device).long()
# Decode the image
with torch.no_grad():
reconstructed = decoder.decode(data)
img = (
((reconstructed[0].cpu().float() + 1) * 127.5).clamp(0, 255).to(torch.uint8)
)
img = img.permute(1, 2, 0).numpy()
img = Image.fromarray(img)
``` |