cleanup

README.md

@@ -1017,33 +1017,6 @@ The most significant parameters for the script are as follows:
 
 - `clip`, default = `None` # Signals the prior to use pre-computed embeddings
 
-#### Loading and Saving the DiffusionPrior model
-
-Two methods are provided, load_diffusion_model and save_diffusion_model, the names being self-explanatory. 
-
-```python
-from dalle2_pytorch.train import load_diffusion_model, save_diffusion_model
-```
-
-##### Loading
-
-    load_diffusion_model(dprior_path, device) 
-        dprior_path : path to saved model(.pth)
-        device      : the cuda device you're running on
-    
-##### Saving
-
-    save_diffusion_model(save_path, model, optimizer, scaler, config, image_embed_dim)
-        save_path : path to save at
-        model     : object of Diffusion_Prior
-        optimizer : optimizer object - see train_diffusion_prior.py for how to create one. 
-            e.g: optimizer = get_optimizer(diffusion_prior.net.parameters(), wd=weight_decay, lr=learning_rate)
-        scaler    : a GradScaler object.
-            e.g: scaler = GradScaler(enabled=amp)
-        config    : config object created in train_diffusion_prior.py - see file for example. 
-        image_embed_dim - the dimension of the image_embedding
-            e.g: 768
-
 ## CLI (wip)
 
 ```bash

dalle2_pytorch/trainer.py

@@ -145,44 +145,6 @@ def split_args_and_kwargs(*args, split_size = None, **kwargs):
         chunk_size_frac = chunk_size / batch_size
         yield chunk_size_frac, (chunked_args, chunked_kwargs)
 
-# 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, loaded_obj
-
-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):
@@ -505,26 +467,20 @@ class DiffusionPriorTrainer(nn.Module):
     @cast_torch_tensor
     @prior_sample_in_chunks
     def p_sample_loop(self, *args, **kwargs):
-        if self.use_ema:
+        model = self.ema_diffusion_prior.ema_model if self.use_ema else self.diffusion_prior
-            return self.ema_diffusion_prior.ema_model.p_sample_loop(*args, **kwargs)
+        return model.p_sample_loop(*args, **kwargs)
-        else:
-            return self.diffusion_prior.p_sample_loop(*args, **kwargs)
 
     @torch.no_grad()
     @cast_torch_tensor
     @prior_sample_in_chunks
     def sample(self, *args, **kwargs):
-        if self.use_ema:
+        model = self.ema_diffusion_prior.ema_model if self.use_ema else self.diffusion_prior
-            return self.ema_diffusion_prior.ema_model.sample(*args, **kwargs)
+        return model.sample(*args, **kwargs)
-        else:
-            return self.diffusion_prior.sample(*args, **kwargs)
 
     @torch.no_grad()
     def sample_batch_size(self, *args, **kwargs):
-        if self.use_ema:
+        model = self.ema_diffusion_prior.ema_model if self.use_ema else self.diffusion_prior
-            return self.ema_diffusion_prior.ema_model.sample_batch_size(*args, **kwargs)
+        return model.sample_batch_size(*args, **kwargs)
-        else:
-            return self.diffusion_prior.sample_batch_size(*args, **kwargs)
 
     @torch.no_grad()
     @cast_torch_tensor