Update README.md

README.md

@@ -1,3 +1,84 @@
----
-license: unknown
----
+---
+license: unknown
+---
+
+it's like [imagenet.int8](https://huggingface.co/datasets/cloneofsimo/imagenet.int8) but 
+* full in1k (including val+test) 1,431,168 samples
+* flux-dev vae, latent (after dequant) channels scaled to N(0,1)
+* quantization uses int8 not uint8 (scaling factor 127/4)
+
+basic decode test:
+```python
+# huggingface-cli download --repo-type dataset main-horse/in1k.int8 --revision flux-1.0-dev --local-dir ./imagenet_int8
+import torch
+from streaming import StreamingDataset
+import streaming.base.util as util
+from diffusers import AutoencoderKL
+from diffusers.image_processor import VaeImageProcessor
+
+# Constants from normalization
+CHANNEL_MEANS = torch.tensor([
+    -0.008, -1.337, 0.335, -0.077, -0.134, 0.320, -1.196, 0.545,
+    -0.159, 0.284, 0.584, 0.062, -0.319, 0.001, -0.859, -0.246
+], device='cuda')
+
+CHANNEL_STDS = torch.tensor([
+    1.996, 3.531, 2.036, 1.428, 1.510, 1.710, 3.108, 2.410,
+    1.810, 2.670, 1.711, 1.941, 2.648, 2.734, 2.250, 2.479
+], device='cuda')
+
+def unnormalize_latents(x: torch.Tensor) -> torch.Tensor:
+    """Undo the N(0,1) normalization"""
+    return x * CHANNEL_STDS[:,None] + CHANNEL_MEANS[:,None]
+
+# Clean up any stale shared memory
+util.clean_stale_shared_memory()
+
+# Load the int8 dataset
+remote_dir = "./imagenet_int8"
+local_dir = "./local_test_dir2"
+dataset = StreamingDataset(
+    local=local_dir,
+    remote=remote_dir,
+    split=None,
+    shuffle=False,
+    batch_size=32
+)
+dataloader = torch.utils.data.DataLoader(dataset, batch_size=32, num_workers=0)
+
+# Get the 5th sample
+batch = next(iter(dataloader))
+i = 5  # Get 5th sample
+
+# Convert int8 latent back to float and reshape
+latent = batch['latent'][i].reshape(16, 1024).cuda().float()
+label = batch['label'][i].item()
+
+print(f"Processing sample {i} with label {label}")
+print(f"Latent shape before processing: {latent.shape}")
+
+# First undo the int8 quantization by scaling
+latent = latent / 31.75  # Should now be ~N(0,1)
+
+# Then undo the normalization to get back to original distribution
+latent = unnormalize_latents(latent)
+
+# Reshape to VAE expected format (1, 16, 32, 32)
+latent = latent.reshape(1, 16, 32, 32)
+print(f"Final latent shape: {latent.shape}")
+print(f"Latent stats after denorm: min={latent.min().item():.3f}, max={latent.max().item():.3f}, mean={latent.mean().item():.3f}, std={latent.std().item():.3f}")
+
+# Load and set up VAE
+vae = AutoencoderKL.from_pretrained('black-forest-labs/FLUX.1-dev', subfolder='vae', device_map=0, attn_implementation='sdpa')
+processor = VaeImageProcessor(vae_scale_factor=2 ** (len(vae.config.block_out_channels) - 1))
+
+# Decode and save
+with torch.no_grad():
+    decoded = vae.decode(latent).sample
+    img = processor.postprocess(decoded, do_denormalize=[True, True])[0]
+    img.save("5th_image_from_int8.png")
+
+print("Saved decoded image as 5th_image_from_int8.png")
+```
+
+open an issue if you see any technical problems (license-related issues not welcome)