Create README.md

README.md

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+See the following code:
+
+## Model Use
+
+```python
+# !pip install diffusers
+from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
+import torch
+import matplotlib.pyplot as plt
+
+
+# load model and scheduler
+model_id = "harveymannering/xfetus-ddpm-v2"
+ddpm = DDPMPipeline.from_pretrained(model_id)  # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
+
+x = torch.randn(1, 3, 128, 128).to(device) # noise
+for i, t in tqdm(enumerate(ddpm.scheduler.timesteps)):
+    model_input = ddpm.scheduler.scale_model_input(x, t)
+    with torch.no_grad():
+        # Conditiong on the 'Fetal brain' class (with index 1) because I am most familar
+        # with what these images look like
+        class_label = torch.ones(1, dtype=torch.int64)
+        noise_pred = ddpm.unet(model_input, t, class_label.to(device))["sample"]
+    x = ddpm.scheduler.step(noise_pred, t, x).prev_sample
+
+plt.imshow(x[0].cpu().detach().numpy())
+plt.show()
+```
+
+## Example Outputs
+
+<img width="608" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/6349716695ab8cce385f450e/uxDp-0svPAp2dCmTK36rf.png">
+
+## Training Loss
+
+<img width="608" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/6349716695ab8cce385f450e/XEZb34rdFYaeFckDMyCYm.png">