Update README.md

README.md

@@ -8,9 +8,11 @@ tags:
 - ddpm
 ---
 
+This model was created for the [Keras code example](https://keras.io/examples/generative/ddim/) on [denoising diffusion implicit models (DDIM)](https://arxiv.org/abs/2010.02502).
+
 ## Model description
 
-The model uses a [U-Net](https://arxiv.org/abs/1505.04597) with identical input and output dimensions. It progressively downsamples and upsamples its input image, adding skip connections between layers having the same resolution. The architecture is a simplified version of the architecture of [DDPM](https://arxiv.org/abs/2006.11239). It consists of convolutional residual blocks and lacks attention layers. The network takes two inputs, the noisy images and the variances of their noise components, which it encodes using [sinusoidal embeddings](https://arxiv.org/abs/1706.03762). More details in the Keras code example.
+The model uses a [U-Net](https://arxiv.org/abs/1505.04597) with identical input and output dimensions. It progressively downsamples and upsamples its input image, adding skip connections between layers having the same resolution. The architecture is a simplified version of the architecture of [DDPM](https://arxiv.org/abs/2006.11239). It consists of convolutional residual blocks and lacks attention layers. The network takes two inputs, the noisy images and the variances of their noise components, which it encodes using [sinusoidal embeddings](https://arxiv.org/abs/1706.03762).
 
 ## Intended uses & limitations
 
@@ -22,7 +24,9 @@ The model is trained on the [Oxford Flowers 102](https://www.tensorflow.org/data
 
 ## Training procedure
 
-The model is trained to denoise noisy images, and can generate images by iteratively denoising pure Gaussian noise. More details in the Keras code example.
+The model is trained to denoise noisy images, and can generate images by iteratively denoising pure Gaussian noise. 
+
+For more details check out the [Keras code example](https://keras.io/examples/generative/ddim/), or the companion [code repository](https://github.com/beresandras/clear-diffusion-keras), with additional features..
 
 ## Training hyperparameters