PyTorch Code for SPADE

Project page Paper GTC 2019 demo Youtube Demo of GauGAN


Clone this repo.

git clone

This code requires PyTorch 1.0 and python 3+. Please install dependencies by

pip install -r requirements.txt

This code also requires the Synchronized-BatchNorm-PyTorch rep.

cd models/networks/
git clone
cp Synchronized-BatchNorm-PyTorch/sync_batchnorm . -rf
cd ../../

Generating Images Using Pretrained Model

Once the dataset is ready. The result images can be generated using pretrained models.

  1. Download the tar of the pretrained models from the Google Drive Folder, save it in ‘checkpoints/’, and run

     cd checkpoints
     tar xvf checkpoints.tar.gz
     cd ../
  2. Generate images using the pretrained model.
     python --name [type]_pretrained --dataset_mode [dataset] --dataroot [path_to_dataset]

    [type]_pretrained is the directory name of the checkpoint file downloaded in Step 1, which should be one of coco_pretrained, ade20k_pretrained, and cityscapes_pretrained. [dataset] can be one of coco, ade20k, and cityscapes, and [path_to_dataset], is the path to the dataset. If you are running on CPU mode, append --gpu_ids -1.

  3. The outputs images are stored at ./results/[type]_pretrained/ by default. You can view them using the autogenerated HTML file in the directory.

Training New Models

New models can be trained with the following commands.

  1. Prepare dataset. To train on the datasets shown in the paper, you can download the datasets and use --dataset_mode option, which will choose which subclass of BaseDataset is loaded. For custom datasets, the easiest way is to use ./data/ by specifying the option --dataset_mode custom, along with --label_dir [path_to_labels] --image_dir [path_to_images]. You also need to specify options such as --label_nc for the number of label classes in the dataset, --contain_dontcare_label to specify whether it has an unknown label, or --no_instance to denote the dataset doesn’t have instance maps.

  2. Train.

# To train on the Facades or COCO dataset, for example.
python --name [experiment_name] --dataset_mode facades --dataroot [path_to_facades_dataset]
python --name [experiment_name] --dataset_mode coco --dataroot [path_to_coco_dataset]

## Testing

Testing is similar to testing pretrained models.

python --name [name_of_experiment] --dataset_mode [dataset_mode] --dataroot [path_to_dataset]

Use --results_dir to specify the output directory. --how_many will specify the maximum number of images to generate. By default, it loads the latest checkpoint. It can be changed using --which_epoch.

Code Structure

  •, the entry point for training and testing.
  • trainers/ harnesses and reports the progress of training.
  • models/ creates the networks, and compute the losses
  • models/networks/: defines the architecture of all models
  • options/: creates option lists using argparse package. More individuals are dynamically added in other files as well. Please see the section below.
  • data/: defines the class for loading images and label maps.


This code repo contains many options. Some options belong to only one specific model, and some options have different default values depending on other options. To address this, the BaseOption class dynamically loads and sets options depending on what model, network, and datasets are used. This is done by calling the static method modify_commandline_options of various classes. It takes in theparser of argparse package and modifies the list of options. For example, since COCO-stuff dataset contains a special label “unknown”, when COCO-stuff dataset is used, it sets --contain_dontcare_label automatically at data/ You can take a look at def gather_options() of options/, or models/network/ to get a sense of how this works.

VAE-Style Training with an Encoder For Style Control and Multi-Modal Outputs

To train our model along with an image encoder to enable multi-modal outputs as in Figure 15 of the paper, please use --use_vae. The model will create netE in addition to netG and netD and train with KL-Divergence loss.

Core Design

In SPADE, the affine layer is learned from semantic segmentation map.

class SPADE(nn.Module):
    def __init__(self, config_text, norm_nc, label_nc):

        assert config_text.startswith('spade')
        parsed ='spade(\D+)(\d)x\d', config_text)
        param_free_norm_type = str(
        ks = int(

        if param_free_norm_type == 'instance':
            self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)
        elif param_free_norm_type == 'syncbatch':
            self.param_free_norm = SynchronizedBatchNorm2d(norm_nc, affine=False)
        elif param_free_norm_type == 'batch':
            self.param_free_norm = nn.BatchNorm2d(norm_nc, affine=False)
            raise ValueError('%s is not a recognized param-free norm type in SPADE'
                             % param_free_norm_type)

        # The dimension of the intermediate embedding space. Yes, hardcoded.
        nhidden = 128

        pw = ks // 2
        self.mlp_shared = nn.Sequential(
            nn.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw),
        self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw)
        self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw)

    def forward(self, x, segmap):

        # Part 1. generate parameter-free normalized activations
        normalized = self.param_free_norm(x)

        # Part 2. produce scaling and bias conditioned on semantic map
        segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest')
        actv = self.mlp_shared(segmap)
        gamma = self.mlp_gamma(actv)
        beta = self.mlp_beta(actv)

        # apply scale and bias
        out = normalized * (1 + gamma) + beta

        return out