diff --git a/README.md b/README.md
index 470b64fd96..6c76122215 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,62 @@
-Super cool library about diffusion models
+# Diffusers
+
+## Library structure:
+
+```
+├── models
+│   ├── dalle2
+│   │   ├── modeling_dalle2.py
+│   │   ├── README.md
+│   │   └── run_dalle2.py
+│   ├── ddpm
+│   │   ├── modeling_ddpm.py
+│   │   ├── README.md
+│   │   └── run_ddpm.py
+│   ├── glide
+│   │   ├── modeling_glide.py
+│   │   ├── README.md
+│   │   └── run_dalle2.py
+│   ├── imagen
+│   │   ├── modeling_dalle2.py
+│   │   ├── README.md
+│   │   └── run_dalle2.py
+│   └── latent_diffusion
+│       ├── modeling_latent_diffusion.py
+│       ├── README.md
+│       └── run_latent_diffusion.py
+├── src
+│   └── diffusers
+│       ├── configuration_utils.py
+│       ├── __init__.py
+│       ├── modeling_utils.py
+│       ├── models
+│       │   └── unet.py
+│       ├── processors
+│       └── samplers
+│           ├── gaussian.py
+├── tests
+│   └── test_modeling_utils.py
+```
+
+## Dummy Example
+```python
+from diffusers import UNetModel, GaussianDiffusion
+import torch
+
+# 1. Load model
+unet = UNetModel.from_pretrained("fusing/ddpm_dummy")
+
+# 2. Do one denoising step with model
+batch_size, num_channels, height, width = 1, 3, 32, 32
+dummy_noise = torch.ones((batch_size, num_channels, height, width))
+time_step = torch.tensor([10])
+image = unet(dummy_noise, time_step)
+
+# 3. Load sampler
+sampler = GaussianDiffusion.from_config("fusing/ddpm_dummy")
+
+# 4. Sample image from sampler passing the model
+image = sampler.sample(model, batch_size=1)
+
+print(image)
+```