[dreambooth] make collate_fn global

examples/dreambooth/train_dreambooth.py

@@ -300,9 +300,10 @@ def __getitem__(self, index):
         example["instance_images"] = self.image_transforms(instance_image)
         example["instance_prompt_ids"] = self.tokenizer(
             self.instance_prompt,
-            padding="do_not_pad",
             truncation=True,
+            padding="max_length",
             max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
         ).input_ids
 
         if self.class_data_root:
@@ -312,14 +313,37 @@ def __getitem__(self, index):
             example["class_images"] = self.image_transforms(class_image)
             example["class_prompt_ids"] = self.tokenizer(
                 self.class_prompt,
-                padding="do_not_pad",
                 truncation=True,
+                padding="max_length",
                 max_length=self.tokenizer.model_max_length,
+                return_tensors="pt",
             ).input_ids
 
         return example
 
 
+def collate_fn(examples, with_prior_preservation=False):
+    input_ids = [example["instance_prompt_ids"] for example in examples]
+    pixel_values = [example["instance_images"] for example in examples]
+
+    # Concat class and instance examples for prior preservation.
+    # We do this to avoid doing two forward passes.
+    if with_prior_preservation:
+        input_ids += [example["class_prompt_ids"] for example in examples]
+        pixel_values += [example["class_images"] for example in examples]
+
+    pixel_values = torch.stack(pixel_values)
+    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
+
+    input_ids = torch.cat(input_ids, dim=0)
+
+    batch = {
+        "input_ids": input_ids,
+        "pixel_values": pixel_values,
+    }
+    return batch
+
+
 class PromptDataset(Dataset):
     "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
 
@@ -510,34 +534,12 @@ def main(args):
         center_crop=args.center_crop,
     )
 
-    def collate_fn(examples):
-        input_ids = [example["instance_prompt_ids"] for example in examples]
-        pixel_values = [example["instance_images"] for example in examples]
-
-        # Concat class and instance examples for prior preservation.
-        # We do this to avoid doing two forward passes.
-        if args.with_prior_preservation:
-            input_ids += [example["class_prompt_ids"] for example in examples]
-            pixel_values += [example["class_images"] for example in examples]
-
-        pixel_values = torch.stack(pixel_values)
-        pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
-        input_ids = tokenizer.pad(
-            {"input_ids": input_ids},
-            padding="max_length",
-            max_length=tokenizer.model_max_length,
-            return_tensors="pt",
-        ).input_ids
-
-        batch = {
-            "input_ids": input_ids,
-            "pixel_values": pixel_values,
-        }
-        return batch
-
     train_dataloader = torch.utils.data.DataLoader(
-        train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn, num_workers=1
+        train_dataset,
+        batch_size=args.train_batch_size,
+        shuffle=True,
+        collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
+        num_workers=1,
     )
 
     # Scheduler and math around the number of training steps.