some cleanup

dalle2_pytorch/train.py

@@ -39,6 +39,50 @@ def groupby_prefix_and_trim(prefix, d):
     kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
     return kwargs_without_prefix, kwargs
 
+# print helpers
+
+def print_ribbon(s, symbol = '=', repeat = 40):
+    flank = symbol * repeat
+    return f'{flank} {s} {flank}'
+
+# saving and loading functions
+
+# for diffusion prior
+
+def load_diffusion_model(dprior_path, device):
+    dprior_path = Path(dprior_path)
+    assert dprior_path.exists(), 'Dprior model file does not exist'
+    loaded_obj = torch.load(str(dprior_path), map_location='cpu')
+
+    # Get hyperparameters of loaded model
+    dpn_config = loaded_obj['hparams']['diffusion_prior_network']
+    dp_config = loaded_obj['hparams']['diffusion_prior']
+    image_embed_dim = loaded_obj['image_embed_dim']['image_embed_dim']
+
+    # Create DiffusionPriorNetwork and DiffusionPrior with loaded hyperparameters
+
+    # DiffusionPriorNetwork
+    prior_network = DiffusionPriorNetwork( dim = image_embed_dim, **dpn_config).to(device)
+
+    # DiffusionPrior with text embeddings and image embeddings pre-computed
+    diffusion_prior = DiffusionPrior(net = prior_network, **dp_config, image_embed_dim = image_embed_dim).to(device)
+
+    # Load state dict from saved model
+    diffusion_prior.load_state_dict(loaded_obj['model'])
+
+    return diffusion_prior
+
+def save_diffusion_model(save_path, model, optimizer, scaler, config, image_embed_dim):
+    # Saving State Dict
+    print_ribbon('Saving checkpoint')
+
+    state_dict = dict(model=model.state_dict(),
+                      optimizer=optimizer.state_dict(),
+                      scaler=scaler.state_dict(),
+                      hparams = config,
+                      image_embed_dim = {"image_embed_dim":image_embed_dim})
+    torch.save(state_dict, save_path+'/'+str(time.time())+'_saved_model.pth')
+
 # exponential moving average wrapper
 
 class EMA(nn.Module):