Update README.md

README.md

@@ -1,3 +1,84 @@
----
-license: apache-2.0
----
+---
+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)
+```