README.md

---
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)
```