README.md

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">