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README.md

@@ -14,3 +14,119 @@ The code for this model can be found in this [GitHub repository](https://github.
 
 ## Usage
 
+As it is a Custom Class Model of the Diffusers library, it can be used as follows:
+
+Setup
+```python	
+import json
+import torch
+import torchvision
+from matplotlib import pyplot as plt
+from tqdm.auto import tqdm
+from torch import nn
+from diffusers import UNet2DModel, DDPMScheduler
+import safetensors
+from huggingface_hub import hf_hub_download
+device = 'mps' if torch.backends.mps.is_available() else 'cuda' if torch.cuda.is_available() else 'cpu'
+```
+Load the ClassConditionedUnet model safetensor:
+```python
+# Custom Class
+class ClassConditionedUnet(nn.Module):
+  def __init__(self, num_classes=10, class_emb_size=10):
+    super().__init__()
+
+    # The embedding layer will map the class label to a vector of size class_emb_size
+    self.class_emb = nn.Embedding(num_classes, class_emb_size)
+
+    # Self.model is an unconditional UNet with extra input channels
+    # to accept the conditioning information (the class embedding)
+    self.model = UNet2DModel(
+        sample_size=28,           # output image resolution. Equal to input resolution
+        in_channels=1 + class_emb_size, # Additional input channels for class cond
+        out_channels=1,           # the number of output channels. Equal to input
+        layers_per_block=3,       # three residual connections (ResNet) per block
+        block_out_channels=(128, 256, 512), # N of output channels for each block. Inverse for upsampling
+        down_block_types=(
+            "DownBlock2D",  # a regular ResNet downsampling block
+            "AttnDownBlock2D",
+            "AttnDownBlock2D",  # a ResNet downsampling block with spatial self-attention
+        ),
+        up_block_types=(
+            "AttnUpBlock2D",  # a ResNet upsampling block with spatial self-attention
+            "AttnUpBlock2D",
+            "UpBlock2D",  # a regular ResNet upsampling block
+        ),
+        dropout = 0.1,  # Dropout prob between Conv1 and Conv2 in a block. From Improved DDPM paper
+    )
+
+  # Forward method takes the class labels as an additional argument
+  def forward(self, x, t, class_labels):
+    bs, ch, w, h = x.shape # x is shape (bs, 1, 28, 28)
+
+    # class conditioning embedding to add as additional input channels
+    class_cond = self.class_emb(class_labels) # Map to embedding dimension
+    class_cond = class_cond.view(bs, class_cond.shape[1], 1, 1).expand(bs, class_cond.shape[1], w, h)
+    # class_cond final shape (bs, 4, 28, 28)
+
+    # Model input is now x and class cond concatenated together along dimension 1
+    # We need provide additional information (the class label)
+    # to every spatial location (pixel) in the image. Not changing the original
+    # pixels of the images, but adding new channels.
+    net_input = torch.cat((x, class_cond), 1) # (bs, 5, 28, 28)
+
+    # Feed this to the UNet alongside the timestep and return the prediction
+    # with image output size
+    return self.model(net_input, t).sample # (bs, 1, 28, 28)
+
+# Define paths to download the model and scheduler
+repo_name = "Huertas97/conditioned-unet-fashion-mnist-non-ema"
+
+# Download the safetensors model file
+model_file_path = hf_hub_download(repo_id=repo_name, filename="fashion_class_cond_unet_model_best.safetensors")
+
+# # Load the Class Conditioned UNet model state dictionary
+state_dict = safetensors.torch.load_file(model_file_path)
+model_classcond_native =  ClassConditionedUnet()
+model_classcond_native.load_state_dict(state_dict).to(device)
+```
+
+Load the DDPMScheduler:
+```python
+# Download and load the scheduler configuration file
+scheduler_file_path = hf_hub_download(repo_id=repo_name, filename="scheduler_config.json")
+
+with open(scheduler_file_path, 'r') as f:
+    scheduler_config = json.load(f)
+
+noise_scheduler = DDPMScheduler.from_config(scheduler_config)
+```	
+
+Use the model to generate images:
+
+```python
+desired_class = [7] # desired class from 0 -> 9
+num_samples = 2  # num of images to generate per class
+
+# Prepare random x to start from
+x = torch.randn(num_samples*len(desired_class), 1, 28, 28).to(device)
+
+# Prepare the desired classes
+y = torch.tensor([[i]*num_samples for i in desired_class]).flatten().to(device)
+
+model_classcond_native = model_classcond_native.to(device)
+
+# Sampling loop
+for i, t in tqdm(enumerate(noise_scheduler.timesteps)):
+  # Get model pred
+  with torch.no_grad():
+      residual = model_classcond_native(x, t, y)
+
+  # Update sample with step
+  x = noise_scheduler.step(residual, t, x).prev_sample
+
+# Show the results
+fig, ax = plt.subplots(1, 1, figsize=(12, 12))
+ax.imshow(torchvision.utils.make_grid(x.detach().cpu().clip(-1, 1), nrow=8)[0], cmap='Greys')
+```
+