VAE: Noisy decoder output

Hello everyone!
I’m trying to implement a simple VAE by following several tutorials ([1], This is the code that I have written:

class VAE(torch.nn.Module):
    def __init__(self,input_shape,h_dim,z_dim):
        #Encoder, image to hidden space
        self.img_2hid = Modules.Linear(input_shape,h_dim)
        # From the hidden we move to z space and we get mu and std
        self.fc_mu,self.fc_std = Modules.Linear(h_dim,z_dim),Modules.Linear(h_dim,z_dim)
        # The decoder from the z space goes back to the hidden
        self.z_2hidden = Modules.Linear(z_dim,h_dim)
        # Finally from the hidden we go back to the input space
        self.hidden_2img = Modules.Linear(h_dim,input_shape)
        self.relu = Modules.Relu()
        self.rloss = torch.nn.MSELoss('sum')

    def encode(self,x):
        x = self.relu(self.img_2hid(x))
        return self.fc_mu(x),torch.log(self.fc_std(x))

    def decode(self,x):
        x = self.relu(self.z_2hidden(x))
        return self.relu(self.hidden_2img(x))

    def normalize(self,x):
        v_min, v_max = x.min(), x.max()
        new_min, new_max = torch.Tensor([0]).float(), torch.Tensor([255]).float()
        x = torch.add(torch.mul(torch.div(torch.sub(x, v_min), torch.sub(v_max, v_min)), torch.sub(new_max, new_min)),new_min)
        return x

    def forward(self,x):
        original = x.shape
        # encode x to get the mu and variance parameters
        mu, log_sigma = self.encode(x.reshape(-1))
        # sample z from q
        sigma = torch.exp(log_sigma)
        epsilon = torch.randn_like(sigma)
        z = mu + sigma * epsilon
        # decoded
        x_hat = self.normalize(self.decode(z)).reshape(original)
        # reconstruction loss
        recon_loss = self.rloss(x_hat,x)
        # kl
        # The formula here is suppose for a normal distribution for q(z|x)
        kl_div = - 0.5 * torch.sum(1 + torch.log(sigma) * 2 - mu.pow(2) - torch.exp(log_sigma*2)) 
        # elbo
        elbo = recon_loss + kl_div
        return elbo,recon_loss,kl_div,x_hat

The problem that I’m having is that the output of the decoder is extremely noisy. For reference by training on this image:
I get:
Any idea why? On the MNIST dataset the VAE works well, but as soon as I pass real images it behaves this way