Update README.md

README.md

@@ -13,12 +13,15 @@ denoise_util.py includes all definitions required to use Cascaded Gaze networks
 
 **v1**
 - ~ 132M params, trained on 256 * 256 RGB patches for intermediate jpg & webp compression artefact removal. It's been trained on about 700k samples (photographs only) at a precision of bf16. Also capable of removing ISO-like noise and gaussian noise.
+- I recommend inputing tensors of [B,3,256,256], with values of floats scaled to 0 - 1.
 
 **Loading v1**
 ``` python
 from denoise_util import CascadedGaze
 from safetensors.torch import load_file
 
+device = "cuda"
+
 img_channel = 3
 width = 60
 enc_blks = [2, 2, 4, 6]
@@ -31,7 +34,44 @@ model = CascadedGaze(img_channel=img_channel,width=width, middle_blk_num=middle_
 
 state_dict = load_file("models/v1.safetensors")
 model.load_state_dict(state_dict)
+model = model.to(device)
 model.requires_grad_(False)
 model.eval()
 ```
-I recommend inputing tensors of [B,3,256,256], with values of floats scaled to 0 - 1.
+
+**Usage**
+- Using https://github.com/ProGamerGov/blended-tiling to handle converting images of arbitrary sizes into 256*256 tiles then back again.
+- You'll need to make ammendments to prevent the batches from being too large for your device.
+- presumes the model was already loaded with code above.
+
+```python
+import torch
+from PIL import Image
+import torchvision
+from blended_tiling import TilingModule
+
+def toimg(tensor):
+    tensor = torch.clamp(tensor, 0.0, 1.0)
+    tensor = tensor * 255
+    tensor = tensor.byte()
+    return torchvision.transforms.functional.to_pil_image(tensor)
+
+# nb: if rgba inputs are anticipated, this won't be sufficient.
+pil_image = Image.open("input.jpg").convert("RGB")
+
+tiling_module = TilingModule(
+    tile_size=[256, 256],
+    tile_overlap=[0.1, 0.1], # you can configure this to taste
+    base_size=pil_image.size,
+)
+
+tensor = torchvision.transforms.functional.to_tensor(pil_image)
+tensor = torch.unsqueeze(tensor,0)
+tiles = tiling_module.split_into_tiles(tensor)
+tiles = tiles.to(device)
+result = model(tiles).cpu()
+result = tiling_module.rebuild_with_masks(result).squeeze()
+
+pil_result = toimg(result)
+```
+