Add color jitter

5 files changed, 56 insertions, 17 deletions

diff --git a/data/csv.py b/data/csv.py
index e1b92c1..818fcd9 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -186,6 +186,7 @@ class VlpnDataModule():
         dropout: float = 0,
         shuffle: bool = False,
         interpolation: str = "bicubic",
+        color_jitter: bool = True,
         template_key: str = "template",
         placeholder_tokens: list[str] = [],
         valid_set_size: Optional[int] = None,
@@ -219,6 +220,7 @@ class VlpnDataModule():
         self.shuffle = shuffle
         self.template_key = template_key
         self.interpolation = interpolation
+        self.color_jitter = color_jitter
         self.valid_set_size = valid_set_size
         self.train_set_pad = train_set_pad if train_set_pad is not None else batch_size
         self.valid_set_pad = valid_set_pad if valid_set_pad is not None else batch_size
@@ -323,7 +325,7 @@ class VlpnDataModule():
             num_buckets=self.num_buckets, progressive_buckets=self.progressive_buckets,
             bucket_step_size=self.bucket_step_size, bucket_max_pixels=self.bucket_max_pixels,
             batch_size=self.batch_size, fill_batch=True, generator=generator,
-            size=self.size, interpolation=self.interpolation,
+            size=self.size, interpolation=self.interpolation, color_jitter=self.color_jitter,
             num_class_images=self.num_class_images, dropout=self.dropout, shuffle=self.shuffle,
         )
 
@@ -343,7 +345,7 @@ class VlpnDataModule():
                 num_buckets=self.num_buckets, progressive_buckets=True,
                 bucket_step_size=self.bucket_step_size, bucket_max_pixels=self.bucket_max_pixels,
                 batch_size=self.batch_size, generator=generator,
-                size=self.size, interpolation=self.interpolation,
+                size=self.size, interpolation=self.interpolation, color_jitter=self.color_jitter,
             )
 
             self.val_dataloader = DataLoader(
@@ -370,6 +372,7 @@ class VlpnDataset(IterableDataset):
         dropout: float = 0,
         shuffle: bool = False,
         interpolation: str = "bicubic",
+        color_jitter: bool = True,
         generator: Optional[torch.Generator] = None,
     ):
         self.items = items
@@ -382,6 +385,7 @@ class VlpnDataset(IterableDataset):
         self.dropout = dropout
         self.shuffle = shuffle
         self.interpolation = interpolations[interpolation]
+        self.color_jitter = color_jitter
         self.generator = generator
 
         self.buckets, self.bucket_items, self.bucket_assignments = generate_buckets(
@@ -446,15 +450,20 @@ class VlpnDataset(IterableDataset):
                 width = int(self.size * ratio) if ratio > 1 else self.size
                 height = int(self.size / ratio) if ratio < 1 else self.size
 
-                image_transforms = transforms.Compose(
-                    [
-                        transforms.Resize(self.size, interpolation=self.interpolation),
-                        transforms.RandomCrop((height, width)),
-                        transforms.RandomHorizontalFlip(),
-                        transforms.ToTensor(),
-                        transforms.Normalize([0.5], [0.5]),
+                image_transforms = [
+                    transforms.Resize(self.size, interpolation=self.interpolation),
+                    transforms.RandomCrop((height, width)),
+                    transforms.RandomHorizontalFlip(),
+                ]
+                if self.color_jitter:
+                    image_transforms += [
+                        transforms.ColorJitter(0.2, 0.1),
                     ]
-                )
+                image_transforms += [
+                    transforms.ToTensor(),
+                    transforms.Normalize([0.5], [0.5]),
+                ]
+                image_transforms = transforms.Compose(image_transforms)
 
                 continue
 
diff --git a/environment.yaml b/environment.yaml
index 1de76bd..418cb22 100644
--- a/environment.yaml
+++ b/environment.yaml
@@ -24,5 +24,6 @@ dependencies:
           - safetensors==0.3.0
           - setuptools==65.6.3
           - test-tube>=0.7.5
+          - timm==0.8.17.dev0
           - transformers==4.27.1
           - triton==2.0.0.post1
diff --git a/train_dreambooth.py b/train_dreambooth.py
index 4c36ae4..48921d4 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -306,7 +306,7 @@ def parse_args():
         "--optimizer",
         type=str,
         default="dadan",
-        choices=["adam", "adam8bit", "lion", "dadam", "dadan", "adafactor"],
+        choices=["adam", "adam8bit", "adan", "lion", "dadam", "dadan", "adafactor"],
         help='Optimizer to use'
     )
     parser.add_argument(
@@ -513,8 +513,6 @@ def main():
         if not embeddings_dir.exists() or not embeddings_dir.is_dir():
             raise ValueError("--embeddings_dir must point to an existing directory")
 
-        embeddings.persist()
-
         added_tokens, added_ids = load_embeddings_from_dir(tokenizer, embeddings, embeddings_dir)
         print(f"Added {len(added_tokens)} tokens from embeddings dir: {list(zip(added_tokens, added_ids))}")
 
@@ -549,6 +547,17 @@ def main():
             eps=args.adam_epsilon,
             amsgrad=args.adam_amsgrad,
         )
+    elif args.optimizer == 'adan':
+        try:
+            import timm.optim
+        except ImportError:
+            raise ImportError("To use Adan, please install the PyTorch Image Models library: `pip install timm`.")
+
+        create_optimizer = partial(
+            timm.optim.Adan,
+            weight_decay=args.adam_weight_decay,
+            eps=args.adam_epsilon,
+        )
     elif args.optimizer == 'lion':
         try:
             import lion_pytorch
diff --git a/train_lora.py b/train_lora.py
index 538a7f7..73b3e19 100644
--- a/train_lora.py
+++ b/train_lora.py
@@ -317,7 +317,7 @@ def parse_args():
         "--optimizer",
         type=str,
         default="dadan",
-        choices=["adam", "adam8bit", "lion", "dadam", "dadan", "adafactor"],
+        choices=["adam", "adam8bit", "adan", "lion", "dadam", "dadan", "adafactor"],
         help='Optimizer to use'
     )
     parser.add_argument(
@@ -544,8 +544,6 @@ def main():
         if not embeddings_dir.exists() or not embeddings_dir.is_dir():
             raise ValueError("--embeddings_dir must point to an existing directory")
 
-        embeddings.persist()
-
         added_tokens, added_ids = load_embeddings_from_dir(tokenizer, embeddings, embeddings_dir)
         print(f"Added {len(added_tokens)} tokens from embeddings dir: {list(zip(added_tokens, added_ids))}")
 
@@ -580,6 +578,17 @@ def main():
             eps=args.adam_epsilon,
             amsgrad=args.adam_amsgrad,
         )
+    elif args.optimizer == 'adan':
+        try:
+            import timm.optim
+        except ImportError:
+            raise ImportError("To use Adan, please install the PyTorch Image Models library: `pip install timm`.")
+
+        create_optimizer = partial(
+            timm.optim.Adan,
+            weight_decay=args.adam_weight_decay,
+            eps=args.adam_epsilon,
+        )
     elif args.optimizer == 'lion':
         try:
             import lion_pytorch
diff --git a/train_ti.py b/train_ti.py
index 6757bde..fc0d68c 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -330,7 +330,7 @@ def parse_args():
         "--optimizer",
         type=str,
         default="dadan",
-        choices=["adam", "adam8bit", "lion", "dadam", "dadan", "adafactor"],
+        choices=["adam", "adam8bit", "adan", "lion", "dadam", "dadan", "adafactor"],
         help='Optimizer to use'
     )
     parser.add_argument(
@@ -679,6 +679,17 @@ def main():
             eps=args.adam_epsilon,
             amsgrad=args.adam_amsgrad,
         )
+    elif args.optimizer == 'adan':
+        try:
+            import timm.optim
+        except ImportError:
+            raise ImportError("To use Adan, please install the PyTorch Image Models library: `pip install timm`.")
+
+        create_optimizer = partial(
+            timm.optim.Adan,
+            weight_decay=args.adam_weight_decay,
+            eps=args.adam_epsilon,
+        )
     elif args.optimizer == 'lion':
         try:
             import lion_pytorch