patch

README.md

@@ -933,7 +933,7 @@ Please find a sample wandb run log at : https://wandb.ai/laion/diffusion-prior/r
 
 Two methods are provided, load_diffusion_model and save_diffusion_model, the names being self-explanatory. 
 
-## from dalle2_pytorch import load_diffusion_model, save_diffusion_model
+## from dalle2_pytorch.train import load_diffusion_model, save_diffusion_model
 
     load_diffusion_model(dprior_path, device) 
 

dalle2_pytorch/__init__.py

@@ -1,4 +1,4 @@
-from dalle2_pytorch.dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder,load_diffusion_model,save_diffusion_model
+from dalle2_pytorch.dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder
 from dalle2_pytorch.dalle2_pytorch import OpenAIClipAdapter
 from dalle2_pytorch.train import DecoderTrainer, DiffusionPriorTrainer
 

dalle2_pytorch/dalle2_pytorch.py

@@ -5,7 +5,6 @@ from functools import partial
 from contextlib import contextmanager
 from collections import namedtuple
 from pathlib import Path
-import time
 
 import torch
 import torch.nn.functional as F
@@ -34,42 +33,6 @@ from rotary_embedding_torch import RotaryEmbedding
 from x_clip import CLIP
 from coca_pytorch import CoCa
 
-# Diffusion Prior model loading and saving functions
-
-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("====================================== 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')
-
 # helper functions
 
 def exists(val):

dalle2_pytorch/train.py

@@ -1,3 +1,4 @@
+import time
 import copy
 from functools import partial
 
@@ -39,6 +40,50 @@ 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):

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.2.2',
+  version = '0.2.4',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',

train_diffusion_prior.py

@@ -6,7 +6,8 @@ import numpy as np
 import torch
 from torch import nn
 from embedding_reader import EmbeddingReader
-from dalle2_pytorch import DiffusionPrior, DiffusionPriorNetwork, load_diffusion_model, save_diffusion_model
+from dalle2_pytorch import DiffusionPrior, DiffusionPriorNetwork
+from dalle2_pytorch.train import load_diffusion_model, save_diffusion_model, print_ribbon
 from dalle2_pytorch.optimizer import get_optimizer
 from torch.cuda.amp import autocast,GradScaler
 
@@ -153,7 +154,7 @@ def train(image_embed_dim,
         os.makedirs(save_path)
 
     # Get image and text embeddings from the servers
-    print("==============Downloading embeddings - image and text====================")
+    print_ribbon("Downloading embeddings - image and text")
     image_reader = EmbeddingReader(embeddings_folder=image_embed_url, file_format="npy")
     text_reader  = EmbeddingReader(embeddings_folder=text_embed_url, file_format="npy")
     num_data_points = text_reader.count