Upload folder using huggingface_hub

README.md

@@ -31,4 +31,86 @@ These include:
 
 So clearly, it doesn't make sense to download entire Imagenet and do VAE everytime. Just download this, `to('cuda')` the entire dataset just to flex, and call it a day.😌
 
-(BTW If you think you'll need higher precision, you can always further fine-tune your model on higher precision. But I doubt that.)
+(BTW If you think you'll need higher precision, you can always further fine-tune your model on higher precision. But I doubt that.)
+
+
+# How to use?
+
+First download this.
+
+```bash
+do huggingface-cli download --repo-type dataset cloneofsimo/imagenet.int8 --local-dir ./vae_mds
+```
+
+Then, you need to install [streaming dataset](https://github.com/mosaicml/streaming) to use this. The dataset is MDS format.
+
+```bash
+pip install mosaicml-streaming
+```
+
+Then, you can use this dataset like this:
+
+```python
+
+from streaming.base.format.mds.encodings import Encoding, _encodings
+import numpy as np
+from typing import Any
+import torch
+from streaming import StreamingDataset
+from diffusers.models import AutoencoderKL
+from diffusers.image_processor import VaeImageProcessor
+
+class uint8(Encoding):
+    def encode(self, obj: Any) -> bytes:
+        return obj.tobytes()
+
+    def decode(self, data: bytes) -> Any:
+        x=  np.frombuffer(data, np.uint8).astype(np.float32)
+        return (x / 255.0 - 0.5) * 24.0
+
+_encodings["uint8"] = uint8
+
+
+remote_train_dir = "./vae_mds" # this is the path you installed this dataset.
+local_train_dir = "./local_train_dir"
+
+train_dataset = StreamingDataset(
+    local=local_train_dir,
+    remote=remote_train_dir,
+    split=None,
+    shuffle=True,
+    shuffle_algo="naive",
+    num_canonical_nodes=1,
+    batch_size = 32
+)
+
+train_dataloader = torch.utils.data.DataLoader(
+    train_dataset,
+    batch_size=32,
+    num_workers=3,
+)
+
+
+
+###### Example Usage. Let's see if we can get the 5th image. BTW shuffle plz
+
+
+model = "stabilityai/your-stable-diffusion-model"
+vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae").to("cuda:0")
+
+
+batch = next(iter(train_dataloader))
+
+i = 5
+vae_latent = batch["vae_output"].reshape(-1, 4, 32, 32)[i:i+1].cuda().float()
+idx = batch["label"][i]
+text_label = batch['label_as_text'][i]
+
+print(f"idx: {idx}, text_label: {text_label}, latent: {vae_latent.shape}")
+# idx: 402, text_label: acoustic guitar, latent: torch.Size([1, 4, 32, 32])
+
+# example decoding
+x = vae.decode(vae_latent.cuda()).sample
+img = VaeImageProcessor().postprocess(image = x.detach(), do_denormalize = [True, True])[0]
+img.save("5th_image.png")
+```