concat batch in collate fn

examples/dreambooth/train_dreambooth.py

@@ -415,33 +415,23 @@ def main():
     )
 
     def collate_fn(examples):
-        def _collate(input_ids, pixel_values):
-            pixel_values = torch.stack([pixel_value for pixel_value in pixel_values])
-            pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
+        input_ids = [example["instance_prompt_ids"] for example in examples]
+        pixel_values = [example["instance_images"] for example in examples]
 
-            input_ids = [input_id for input_id in input_ids]
-            input_ids = tokenizer.pad(
-                {"input_ids": input_ids},
-                padding=True,
-                return_tensors="pt",
-            ).input_ids
-            return input_ids, pixel_values
+        # concat class and instance examples for prior preservation
+        if args.with_prior_preservation:
+            input_ids += [example["class_prompt_ids"] for example in examples]
+            pixel_values += [example["class_images"] for example in examples]
 
-        instance_prompt_ids = [example["instance_prompt_ids"] for example in examples]
-        instance_images = [example["instance_images"] for example in examples]
-        instance_prompt_ids, instance_images = _collate(instance_prompt_ids, instance_images)
+        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=True, return_tensors="pt").input_ids
 
         batch = {
-            "instance_images": instance_images,
-            "instance_prompt_ids": instance_prompt_ids,
+            "input_ids": input_ids,
+            "pixel_values": pixel_values,
         }
-
-        if args.with_prior_preservation:
-            class_prompt_ids = [example["class_prompt_ids"] for example in examples]
-            class_images = [example["class_images"] for example in examples]
-            class_prompt_ids, class_images = _collate(class_prompt_ids, class_images)
-            batch["class_images"] = class_images
-            batch["class_prompt_ids"] = class_prompt_ids
         return batch
 
     train_dataloader = torch.utils.data.DataLoader(
@@ -503,15 +493,8 @@ def main():
         for step, batch in enumerate(train_dataloader):
             with accelerator.accumulate(unet):
                 # Convert images to latent space
-                if args.with_prior_preservation:
-                    images = torch.cat([batch["instance_images"], batch["class_images"]], dim=0)
-                    input_ids = torch.cat([batch["instance_prompt_ids"], batch["class_prompt_ids"]], dim=0)
-                else:
-                    images = batch["instance_images"]
-                    input_ids = batch["instance_prompt_ids"]
-
                 with torch.no_grad():
-                    latents = vae.encode(images).latent_dist.sample()
+                    latents = vae.encode(batch["pixel_values"]).latent_dist.sample()
                     latents = latents * 0.18215
 
                 # Sample noise that we'll add to the latents
@@ -528,7 +511,7 @@ def main():
 
                 # Get the text embedding for conditioning
                 with torch.no_grad():
-                    encoder_hidden_states = text_encoder(input_ids)[0]
+                    encoder_hidden_states = text_encoder(batch["input_ids"])[0]
 
                 # Predict the noise residual
                 noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample