Edit README.md

README.md

@@ -194,6 +194,17 @@ We will train the diffusion model on the MNIST dataset using the **diffusers** l
 | **Skip Connections**         | Yes                                           | Yes                                              |
 | **Time Embedding**           | No                                            | Yes                                              |
 
+## Results
+
+Here a visual results from the U-Net models:
+
+**Diffusion U-Net2D**  
+![Diffusion U-Net Example result](images/diffuse_denoise_mnist.png)
+
+**Conditional GAN U-Net (cGAN)**  
+![cGAN U-Net Example result](images/unet_denoise_mnist.png)
+
+
 ### Conclusion
 In this section, we have outlined the architecture and training process for a Diffusion model using a U-Net. This model is trained to perform image denoising, progressively refining noisy images into clean ones. We compared it with the U-Net used in cGANs, highlighting the key differences and how they are tailored to their respective tasks.